Polyhedral sensor calibration target for calibrating multiple types of sensors

ABSTRACT

A polyhedral sensor target includes multiple surfaces. A housing, such as a vehicle, may include a camera and a distance measurement sensor, such as a light detection and ranging (LIDAR) sensor. The housing may move between different positions during calibration, for instance by being rotated atop a turntable. The camera and distance measurement sensor may both capture data during calibration, from which visual and distance measurement representations of the polyhedral sensor target are identified. The camera and distance measurement sensor are calibrated based on their respective representations, for example by mapping vertices within the representations to the same location.

TECHNICAL FIELD

The present technology generally pertains to calibration of sensors thatare used by vehicles. More specifically, the present technology pertainsto use of combined sensor calibration targets that are polyhedral andthat are used to calibrate multiple types of vehicle sensors, such ascameras and light detection and ranging (LIDAR) sensor systems.

BACKGROUND

An autonomous vehicle is a motorized vehicle that can navigate without ahuman driver. An exemplary autonomous vehicle includes a plurality ofsensor systems, such as, but not limited to, a camera sensor system, alight detection and ranging (LIDAR) sensor system, or a radio detectionand ranging (RADAR) sensor system, amongst others. The autonomousvehicle operates based upon sensor signals output by the sensor systems.Specifically, the sensor signals are provided to an internal computingsystem in communication with the plurality of sensor systems, wherein aprocessor executes instructions based upon the sensor signals to controla mechanical system of the autonomous vehicle, such as a vehiclepropulsion system, a braking system, or a steering system Similarsensors may also be mounted onto non-autonomous vehicles, for exampleonto vehicles whose sensor data is used to generate or update streetmaps.

A wide range of manufacturing defects or discrepancies can exist invehicles, sensors, and mounting hardware that affixes the sensors to thevehicles. Because of these discrepancies, different sensors mounted todifferent vehicles may capture slightly different data, even when thosevehicles are at the exact same position, and even when the vehicles arebrand new. For example, a lens of one camera may be warped slightly (orinclude some other imperfection) compared to a lens of another camera,one vehicle may include a newer hardware revision or version of aparticular sensor than another, one vehicle's roof may be a fewmillimeters higher or lower than another vehicle's roof, or a skewedscrew used in a mounting structure for a sensor on one vehicle may tiltthe mounting structure slightly. Such imperfections and variations inmanufacturing can impact sensor readings and mean that there no twovehicles capture sensor readings in quite the same way, and thus no twovehicles interpret their surroundings via sensor readings in quite thesame way. With use, vehicles can drift even further apart in theirsensor readings due to exposure to the elements, for example throughexposure to heat, rain, dust, frost, rocks, pollution, vehicularcollisions, all of which can further damage or otherwise impact avehicle or its sensor.

Sensors typically capture data and provide results in a standardizedmanner that does not, by itself, test or account for intrinsicproperties of each sensor, such as the position and angle of the sensoror properties of a lens, or for extrinsic relationships between sensorsthat capture data from similar areas. Because of this, it can be unclearwhether a discrepancy in measurements between two vehicles can beattributed to an actual difference in environment or simply differentproperties of vehicle sensors. Because autonomous vehicles are trustedwith human lives, it is imperative that autonomous vehicles have asrobust an understanding of their environments as possible, otherwise avehicle might perform an action that it should not perform, or fail toperform an action that it should perform, either of which can result ina vehicular accident and put human lives at risk. Other sensor-ladenvehicles, such as those that collect data for maps or street-levelimagery, can produce unreliable maps or images if they cannot accountfor the properties of their sensors, which can then in turn confuse bothhuman vehicles and autonomous vehicles that rely on those maps, againrisking human life.

There is a need for improved techniques and technologies for calibratingsensors of autonomous vehicles.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-recited and other advantages and features of the presenttechnology will become apparent by reference to specific implementationsillustrated in the appended drawings. A person of ordinary skill in theart will understand that these drawings only show some examples of thepresent technology and would not limit the scope of the presenttechnology to these examples. Furthermore, the skilled artisan willappreciate the principles of the present technology as described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 illustrates an autonomous vehicle and remote computing systemarchitecture.

FIG. 2A illustrates a camera calibration target with a checkerboardpattern on a planar substrate.

FIG. 2B illustrates a camera calibration target with a ArUco pattern ona planar substrate.

FIG. 2C illustrates a camera calibration target with a crosshair patternon a planar substrate.

FIG. 2D illustrates a camera calibration target with a dot pattern on aplanar substrate.

FIG. 2E illustrates a RADAR sensor calibration target with a trihedralshape.

FIG. 2F illustrates a combined sensor calibration target that usesapertures from a planar substrate surrounded by visual markings tocalibrate a camera and a LiDAR sensor.

FIG. 2G illustrates a combined sensor calibration target that ispolyhedral and includes markings on multiple surfaces.

FIG. 3 illustrates a top-down view of a hallway calibration environmentin which a vehicle traverses a drive path along which the vehicle isflanked by vehicle sensor calibration targets.

FIG. 4 illustrates a perspective view of a dynamic scene calibrationenvironment in which a turntable that is at least partially surroundedby vehicle camera calibration targets rotates a vehicle so that thevehicle can perform intrinsic calibration of its camera sensors.

FIG. 5A illustrates a perspective view of a dynamic scene calibrationenvironment in which a turntable that is at least partially surroundedby combined camera and LIDAR sensor calibration targets and RADAR sensorcalibration targets so that the vehicle can perform extrinsiccalibration of its camera, LIDAR, and RADAR sensors.

FIG. 5B illustrates a perspective view of a dynamic scene calibrationenvironment in which a turntable that is at least partially surroundedby combined polyhedral sensor calibration targets so that the vehiclecan perform extrinsic calibration of its camera, LIDAR, and RADARsensors.

FIG. 6 illustrates a top-down view of a dynamic scene calibrationenvironment in which a turntable that is at least partially surroundedby various types of vehicle sensor calibration targets.

FIG. 7 illustrates a system architecture of a dynamic scene calibrationenvironment.

FIG. 8 illustrates vehicle operations for sensor calibration for atleast a camera and a distance measurement sensor.

FIG. 9 illustrates vehicle operations for sensor calibration for asecondary distance measurement sensor in addition to the camera anddistance measurement sensor of FIG. 8.

FIG. 10 is a flow diagram illustrating operation of a calibrationenvironment.

FIG. 11 is a flow diagram illustrating operations for intrinsiccalibration of a vehicle sensor using a dynamic scene.

FIG. 12 is a flow diagram illustrating operations for extrinsiccalibration of two sensors in relation to each other using a dynamicscene.

FIG. 13 is a flow diagram illustrating operations for interactionsbetween the vehicle and the turntable.

FIG. 14A is a flow diagram illustrating operations for interactionsbetween the vehicle and a lighting system.

FIG. 14B is a flow diagram illustrating operations for interactionsbetween the vehicle and a target control system.

FIG. 15 shows an example of a system for implementing certain aspects ofthe present technology.

DETAILED DESCRIPTION

Various examples of the present technology are discussed in detailbelow. While specific implementations are discussed, it should beunderstood that this is done for illustration purposes only. A personskilled in the relevant art will recognize that other components andconfigurations may be used without parting from the spirit and scope ofthe present technology. In some instances, well-known structures anddevices are shown in block diagram form in order to facilitatedescribing one or more aspects. Further, it is to be understood thatfunctionality that is described as being carried out by certain systemcomponents may be performed by more or fewer components than shown.

Calibration may be performed using a polyhedral sensor target thatincludes multiple surfaces. A housing, such as a vehicle, may include acamera and a distance measurement sensor, such as a light detection andranging (LIDAR) sensor. The housing may move between different positionsduring calibration, for instance by being rotated atop a turntable. Thecamera and distance measurement sensor may both capture data duringcalibration, from which visual and distance measurement representationsof the polyhedral sensor target are identified. The camera and distancemeasurement sensor are calibrated based on their respectiverepresentations, for example by mapping vertices within therepresentations to the same location.

The disclosed technologies address a need in the art for improvements tovehicle sensor calibration technologies. Use of a polyhedral sensortarget improves the functioning of sensor calibration by enablingmultiple sensors—such as cameras and distance measurement sensors—to becalibrated together using a single sensor calibration target. Thethree-dimensional nature of the polyhedral sensor target also betterensures that three dimensional objects in the real world will beappropriately recognized when the vehicle is later used in the realworld. Calibration using the polyhedral sensor target transform vehiclesensors from an uncalibrated state to a calibrated state, and improveruntime-efficiency, space-efficiency, comprehensiveness of calibration,and consistency of vehicle sensor calibration over prior calibrationtechniques. The vehicle, its sensors, the vehicle's internal computingdevice, and the polyhedral sensor target itself are integral to thetechnology.

FIG. 1 illustrates an autonomous vehicle and remote computing systemarchitecture.

The autonomous vehicle 102 can navigate about roadways without a humandriver based upon sensor signals output by sensor systems 180 of theautonomous vehicle 102. The autonomous vehicle 102 includes a pluralityof sensor systems 180 (a first sensor system 104 through an Nth sensorsystem 106). The sensor systems 180 are of different types and arearranged about the autonomous vehicle 102. For example, the first sensorsystem 104 may be a camera sensor system and the Nth sensor system 106may be a Light Detection and Ranging (LIDAR) sensor system. Otherexemplary sensor systems include radio detection and ranging (RADAR)sensor systems, Electromagnetic Detection and Ranging (EmDAR) sensorsystems, Sound Navigation and Ranging (SONAR) sensor systems, SoundDetection and Ranging (SODAR) sensor systems, Global NavigationSatellite System (GNSS) receiver systems such as Global PositioningSystem (GPS) receiver systems, accelerometers, gyroscopes, inertialmeasurement units (IMU), infrared sensor systems, laser rangefindersystems, ultrasonic sensor systems, infrasonic sensor systems,microphones, or a combination thereof. While four sensors 180 areillustrated coupled to the autonomous vehicle 102, it should beunderstood that more or fewer sensors may be coupled to the autonomousvehicle 102.

The autonomous vehicle 102 further includes several mechanical systemsthat are used to effectuate appropriate motion of the autonomous vehicle102. For instance, the mechanical systems can include but are notlimited to, a vehicle propulsion system 130, a braking system 132, and asteering system 134. The vehicle propulsion system 130 may include anelectric motor, an internal combustion engine, or both. The brakingsystem 132 can include an engine brake, brake pads, actuators, and/orany other suitable componentry that is configured to assist indecelerating the autonomous vehicle 102. In some cases, the brakingsystem 132 may charge a battery of the vehicle through regenerativebraking. The steering system 134 includes suitable componentry that isconfigured to control the direction of movement of the autonomousvehicle 102 during navigation.

The autonomous vehicle 102 further includes a safety system 136 that caninclude various lights and signal indicators, parking brake, airbags,etc. The autonomous vehicle 102 further includes a cabin system 138 thatcan include cabin temperature control systems, in-cabin entertainmentsystems, etc.

The autonomous vehicle 102 additionally comprises an internal computingsystem 110 that is in communication with the sensor systems 180 and thesystems 130, 132, 134, 136, and 138. The internal computing systemincludes at least one processor and at least one memory havingcomputer-executable instructions that are executed by the processor. Thecomputer-executable instructions can make up one or more servicesresponsible for controlling the autonomous vehicle 102, communicatingwith remote computing system 150, receiving inputs from passengers orhuman co-pilots, logging metrics regarding data collected by sensorsystems 180 and human co-pilots, etc.

The internal computing system 110 can include a control service 112 thatis configured to control operation of the vehicle propulsion system 130,the braking system 208, the steering system 134, the safety system 136,and the cabin system 138. The control service 112 receives sensorsignals from the sensor systems 180 as well communicates with otherservices of the internal computing system 110 to effectuate operation ofthe autonomous vehicle 102. In some embodiments, control service 112 maycarry out operations in concert one or more other systems of autonomousvehicle 102.

The internal computing system 110 can also include a constraint service114 to facilitate safe propulsion of the autonomous vehicle 102. Theconstraint service 116 includes instructions for activating a constraintbased on a rule-based restriction upon operation of the autonomousvehicle 102. For example, the constraint may be a restriction uponnavigation that is activated in accordance with protocols configured toavoid occupying the same space as other objects, abide by traffic laws,circumvent avoidance areas, etc. In some embodiments, the constraintservice can be part of the control service 112.

The internal computing system 110 can also include a communicationservice 116. The communication service can include both software andhardware elements for transmitting and receiving signals from/to theremote computing system 150. The communication service 116 is configuredto transmit information wirelessly over a network, for example, throughan antenna array that provides personal cellular (long-term evolution(LTE), 3G, 4G, 5G, etc.) communication.

In some embodiments, one or more services of the internal computingsystem 110 are configured to send and receive communications to remotecomputing system 150 for such reasons as reporting data for training andevaluating machine learning algorithms, requesting assistance fromremote computing system 150 or a human operator via remote computingsystem 150, software service updates, ridesharing pickup and drop offinstructions etc.

The internal computing system 110 can also include a latency service118. The latency service 118 can utilize timestamps on communications toand from the remote computing system 150 to determine if a communicationhas been received from the remote computing system 150 in time to beuseful. For example, when a service of the internal computing system 110requests feedback from remote computing system 150 on a time-sensitiveprocess, the latency service 118 can determine if a response was timelyreceived from remote computing system 150 as information can quicklybecome too stale to be actionable. When the latency service 118determines that a response has not been received within a threshold, thelatency service 118 can enable other systems of autonomous vehicle 102or a passenger to make necessary decisions or to provide the neededfeedback.

The internal computing system 110 can also include a user interfaceservice 120 that can communicate with cabin system 138 in order toprovide information or receive information to a human co-pilot or humanpassenger. In some embodiments, a human co-pilot or human passenger maybe required to evaluate and override a constraint from constraintservice 114, or the human co-pilot or human passenger may wish toprovide an instruction to the autonomous vehicle 102 regardingdestinations, requested routes, or other requested operations.

The internal computing system 110 can, in some cases, include at leastone computing system 1500 as illustrated in or discussed with respect toFIG. 15, or may include at least a subset of the components illustratedin FIG. 15 or discussed with respect to computing system 1500.

As described above, the remote computing system 150 is configured tosend/receive a signal from the autonomous vehicle 140 regardingreporting data for training and evaluating machine learning algorithms,requesting assistance from remote computing system 150 or a humanoperator via the remote computing system 150, software service updates,rideshare pickup and drop off instructions, etc.

The remote computing system 150 includes an analysis service 152 that isconfigured to receive data from autonomous vehicle 102 and analyze thedata to train or evaluate machine learning algorithms for operating theautonomous vehicle 102. The analysis service 152 can also performanalysis pertaining to data associated with one or more errors orconstraints reported by autonomous vehicle 102.

The remote computing system 150 can also include a user interfaceservice 154 configured to present metrics, video, pictures, soundsreported from the autonomous vehicle 102 to an operator of remotecomputing system 150. User interface service 154 can further receiveinput instructions from an operator that can be sent to the autonomousvehicle 102.

The remote computing system 150 can also include an instruction service156 for sending instructions regarding the operation of the autonomousvehicle 102. For example, in response to an output of the analysisservice 152 or user interface service 154, instructions service 156 canprepare instructions to one or more services of the autonomous vehicle102 or a co-pilot or passenger of the autonomous vehicle 102.

The remote computing system 150 can also include a rideshare service 158configured to interact with ridesharing applications 170 operating on(potential) passenger computing devices. The rideshare service 158 canreceive requests to be picked up or dropped off from passengerridesharing app 170 and can dispatch autonomous vehicle 102 for thetrip. The rideshare service 158 can also act as an intermediary betweenthe ridesharing app 170 and the autonomous vehicle wherein a passengermight provide instructions to the autonomous vehicle to 102 go around anobstacle, change routes, honk the horn, etc.

The rideshare service 158 as depicted in FIG. 1 illustrates a vehicle102 as a triangle en route from a start point of a trip to an end pointof a trip, both of which are illustrated as circular endpoints of athick line representing a route traveled by the vehicle. The route maybe the path of the vehicle from picking up the passenger to dropping offthe passenger (or another passenger in the vehicle), or it may be thepath of the vehicle from its current location to picking up anotherpassenger.

The remote computing system 150 can, in some cases, include at least onecomputing system 1500 as illustrated in or discussed with respect toFIG. 15, or may include at least a subset of the components illustratedin FIG. 15 or discussed with respect to computing system 1500.

FIG. 2A illustrates a camera calibration target with a checkerboardpattern on a planar substrate.

The sensor calibration target 200A illustrated in FIG. 2A is a planarboard made from a substrate 205, with a pattern 210A printed, stamped,engraved, imprinted, or otherwise marked thereon. The pattern 210A ofFIG. 2A is a checkerboard pattern. The substrate 205 may be paper,cardboard, plastic, metal, foam, or some combination thereof. Thesubstrate 205 may in some cases include a translucent or transparentsurface upon which the pattern 210A is printed, and which a light sourcemay provide illumination through. The substrate 205 may in some casesinclude a retroreflective surface upon which the pattern 210A isprinted. The retroreflective property of the surface may be inherent tothe material of the substrate 205 or may be a separate layer applied tothe surface of the substrate, for example by adhering a retroreflectivematerial to the substrate 205 or by painting (e.g., via a brush, roller,or aerosol spray) the substrate 205 with a retroreflective paint. Areflective or retroreflective property may in some cases improvedetection using RADAR, LiDAR, or other EmDAR sensors. The material andshape of the substrate 205 may also be selected such that the materialand/or shape produces a high amount of acoustic resonance or acousticresponse to improve detection using SONAR or SODAR sensors. In somecases, the substrate 205, and therefore the target 200A, may be concave,convex, otherwise curved, or some combination thereof. The substrate 205may in some cases include devices, such as speakers, heat sources, orlight sources, that allow improved detection by microphones, infraredsensors, or cameras, respectively.

The sensor calibration target 200A illustrated in FIG. 2A is useful forcalibration of a camera of the vehicle, or other sensor that capturesvisual data. In particular, a camera with a pattern/image/featurerecognition system running on computer system 110 can identify thecheckerboard pattern 210A of FIG. 2A, and can identify pointsrepresenting vertices between the dark (black) and light (white)checkers. By drawing lines connecting these points, the camera andcomputer system 110 can generate a grid. If the camera has a wide-anglelens, such as a fisheye lens or a barrel lens, the resulting grid willbe warped so that some checkers will appear curved rather than straight,and so that checkers near the edges of the camera's point of view willappear more squashed, while checkers near the center of the camera'spoint of view will appear larger and more even. A rectilinear lensprovides a similar, is opposite, effect. Based on prior knowledge ofwhat the checkerboard pattern and resulting grid should look like (e.g.,straight edges for each checker), and its original dimensions, comparedagainst what its representation looks like as captured by the camera,the camera and computing system 110 may identify the distortion/warpingeffect of the lens and counteract this distortion/warping effect byapplying an opposite distortion/warping effect. The camera and computingsystem 110 may also identify other parameters of the camera this way,such as position parameters (x, y, z, roll, pitch, yaw), any lens colorto be filtered out, any crack or defect in the lens to be filtered out,or a combination thereof.

The sensor calibration target 200A illustrated in FIG. 2A is useful fordetection by, and calibration of, a distance measurement sensor of thevehicle, such as a LIDAR, SONAR, SODAR, or radar sensor of the vehicle,at least in that the shape of the planar substrate 205 can be detectedby the distance measurement sensor. For example, flat planar visiontargets such as the target 200A can be detected by lidar by relying onplanar geometry estimates and using the returned intensity. While FIG.2A illustrates a square or rectangular substrate 205, the substrate 205may be circular, semicircular, ellipsoidal, triangular, quadrilateral(trapezoid, parallelogram), pentagonal, hexagonal, heptagonal,octagonal, nonagonal, decagonal, otherwise polygonal, or somecombination thereof.

In some cases, the sensor calibration target 200A illustrated in FIG. 2Amay also function as an infrared sensor target when paired with a heatsource such as a heat lamp, an incandescent lamp, or a halogen lamp. Inparticular, the dark checkers may be darker than the substrate 205 andtherefore may absorb more heat than the substrate 205, and may thus bedistinguishable from the substrate 205 by an infrared sensor such as aninfrared camera.

FIG. 2B illustrates a camera calibration target with a ArUco pattern ona planar substrate.

The sensor calibration target 200B illustrated in FIG. 2B, like thesensor calibration target 200A illustrated in FIG. 2A, includes a planarboard made from a substrate 205, with a pattern 210B printed, stamped,engraved, imprinted, or otherwise marked thereon. The pattern 210Billustrated in FIG. 2B is an ArUco marker pattern, which includes blackborder and an inner binary matrix/grid (e.g., each square is dark/blackor light/white) which determines its identifier.

By detecting the AuUco pattern, the camera and computing system 110 ofthe vehicle also identify a grid, similarly to the checkerboard, thoughpotentially with fewer points, as some areas of the ArUco pattern mayinclude contiguous dark/black squares or contiguous light/white squares.By identifying the grid from the representation of the ArUco targetcaptured by the camera (e.g. with lens distortion such as parabolicdistortion), and comparing it to a known reference image of the ArUcopattern (e.g., without any distortion), any distortions or otherdifferences may be identified, and appropriate corrections may begenerated to counteract these distortions or other differences.

The substrate 205 of FIG. 2B may include or be coated with anypreviously-discussed substrate material and may be warped or shaped inany manner or include any devices discussed with respect to thesubstrate 205 of FIG. 2A, and therefore may be detected by, and beuseful to calibrate a distance measurement sensor of the vehicle, suchas a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, and may bedetected by a microphone or infrared sensor of the vehicle as well.

In some cases, the sensor calibration target 200B illustrated in FIG. 2Bmay also function as an infrared sensor target when paired with a heatsource such as a heat lamp, an incandescent lamp, or a halogen lamp. Inparticular, the dark squares/checkers/areas of the ArUco pattern may bedarker than the substrate 205 and therefore may absorb more heat thanthe substrate 205, and may thus be distinguishable from the substrate205 by an infrared sensor such as an infrared camera.

FIG. 2C illustrates a camera calibration target with a crosshair patternon a planar substrate.

The sensor calibration target 200C illustrated in FIG. 2C, like thesensor calibration target 200A illustrated in FIG. 2A, includes a planarboard made from a substrate 205, with a pattern 210C printed, stamped,engraved, imprinted, or otherwise marked thereon. The pattern 210Cillustrated in FIG. 2C is an crosshair marker pattern, which includesfour dark/black lines and two dark/black circles centered on alight/white background, and with a gap in the dark/black lines near butnot at the center, effectively leaving a “+” symbol in the very center.

The camera and computing system 110 can identify the target 200C byidentifying the circles, the lines, and the intersections of the same.In doing so, the crosshair pattern is identified from the representationof the target 200C captured by the camera (e.g. with lens distortion),and can be compared it to a known reference image of the crosshairpattern target 200C (e.g., without any distortion). As with thecheckerboard and ArUco targets, any distortions or other differences maybe identified, and appropriate corrections may be generated tocounteract these distortions or other differences.

The substrate 205 of FIG. 2C may include or be coated with anypreviously-discussed substrate material and may be warped or shaped inany manner or include any devices discussed with respect to thesubstrate 205 of FIG. 2A, and therefore may be detected by, and beuseful to calibrate a distance measurement sensor of the vehicle, suchas a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, and may bedetected by a microphone or infrared sensor of the vehicle as well.

In some cases, the sensor calibration target 200C illustrated in FIG. 2Cmay also function as an infrared sensor target when paired with a heatsource such as a heat lamp, an incandescent lamp, or a halogen lamp. Inparticular, the dark lines of the pattern 210C may be darker than thesubstrate 205 and therefore may absorb more heat than the substrate 205,and may thus be distinguishable from the substrate 205 by an infraredsensor such as an infrared camera.

FIG. 2D illustrates a camera calibration target with a dot pattern on aplanar substrate.

The sensor calibration target 200D illustrated in FIG. 2D, like thesensor calibration target 200A illustrated in FIG. 2A, includes a planarboard made from a substrate 205, with a pattern 210D printed, stamped,engraved, imprinted, or otherwise marked thereon. The pattern 210Dillustrated in FIG. 2D may be referred to as a dot pattern or a polkadot pattern, and includes an arrangement of circular dots. In analternate embodiment, the dots may be semicircular, oval, square,triangular, rectangular, another polygonal shape, or some combinationthereof. The arrangement of circular dots in the pattern 210D isessentially a two-dimensional (2D) “array” or “matrix” or “grid” of dotsin which every other row is offset by half a cell, or in which everyother column is offset by half a cell. This pattern 200D may also bedescribed as a 2D “array” or “matrix” or “grid” of dots that has beenrotated diagonally by approximately 45 degrees. This pattern 210D mayalso be described as two 2D “array” or “matrix” or “grid” patterns ofdots that are offset from one another. Alternate patterns may be usedinstead, such as patterns with a single “array” or “matrix” or “grid”pattern that is not rotated or offset in any way.

The camera and computing system 110 can identify the target 200D byidentifying the dots in the pattern 200D. In doing so, the vehiclesystem can identify the dot pattern 210D in the representation of thetarget 200D captured by the camera (e.g. with lens distortion), and canbe compared it to a known reference image of the crosshair patterntarget 200D (e.g., without any distortion). As with the checkerboard andArUco targets, any distortions or other differences may be identified,and appropriate corrections may be generated to counteract thesedistortions or other differences. For instance, because straight linesshould be drawable connecting multiple dots of the pattern 200D, anycorrections generated may remove or reduce curvature in such lines fromthe representation of the target 200D captured by the camera.

The substrate 205 of FIG. 2D may include or be coated with anypreviously-discussed substrate material and may be warped or shaped inany manner or include any devices discussed with respect to thesubstrate 205 of FIG. 2A, and therefore may be detected by, and beuseful to calibrate a distance measurement sensor of the vehicle, suchas a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, and may bedetected by a microphone or infrared sensor of the vehicle as well.

In some cases, the sensor calibration target 200D illustrated in FIG. 2Dmay also function as an infrared sensor target when paired with a heatsource such as a heat lamp, an incandescent lamp, or a halogen lamp. Inparticular, the dark dots of the pattern 210D may be darker than thesubstrate 205 and therefore may absorb more heat than the substrate 205,and may thus be distinguishable from the substrate 205 by an infraredsensor such as an infrared camera.

While the only patterns 210A-D discussed with respect to camera sensortargets are checkerboard patterns 210A, ArUco patterns 210B, crosshairpatterns 210C, and dot patterns 200D, other patterns that are notdepicted herein can additionally or alternatively be used. For example,bar codes, quick response (QR) codes, Aztec codes, Semacodes, DataMatrix codes, PDF417 codes, MaxiCodes, Shotcodes, High Capacity ColorBarcodes (HCCB), or combinations thereof may be used in place of or inaddition to any of the patterns 210A-D that can be recognized using thecamera and computing device 110.

FIG. 2E illustrates a distance measurement sensor calibration targetwith a trihedral shape.

The sensor calibration target 220 of FIG. 2E is made to be detected by,and use for calibration of, a distance measurement sensor, such as aradar sensor (or LIDAR, SONAR, or SODAR) of the vehicle. In particular,the sensor calibration target 220 of FIG. 2E is trihedral in shape, andmay be a concave or convex trihedral corner, essentially a triangularcorner of a cube. Alternately, it may be a different shape, such as acorner of a different polyhedron (at least portions of all faces of thepolyhedron that touch a particular vertex). Such a shape, especiallywhen concave and where perpendicular faces are included, produces aparticularly strong radar echo and thus a particularly strong radarcross section (RCS) because incoming radio waves are backscattered bymultiple reflection. The RCS of the trihedral corner target is given by:σ=(4·π·a⁴)/(3·λ²), where a is the length of the side edges of the threetriangles, and λ is a wavelength of radar transmitter.

The substrate 205 of the sensor calibration target 220 of FIG. 2E mayinclude or be coated with any previously-discussed substrate materialand may be warped or shaped in any manner, or include any devices,discussed with respect to the substrate 205 of FIG. 2A. In oneembodiment, the substrate 205 of the sensor calibration target 220 ofFIG. 2E may be metal, may be electrically conductive, may be reflectiveor retroreflective, or some combination thereof.

FIG. 2F illustrates a combined distance measurement sensor and cameracalibration target with apertures from a planar substrate that aresurrounded by visually recognizable markings.

The combined distance measurement sensor and camera calibration target250 of FIG. 2F includes multiple apertures 225 in a substrate 205, andincludes visual markings or patterns 230 at least partially surroundingeach aperture. In particular, the target 250 includes four symmetricalcircular or ellipsoid apertures 225 from a light/white substrate 205with symmetrical dark/black circular or ellipsoid rings 230 around theapertures, with three of the apertures/ring combinations being a firstsize (e.g., apertures being 30 cm in diameter and the correspondingrings slightly larger) and a fourth aperture/ring combination 240 beinga second size (e.g., the aperture being 26 cm in diameter and thecorresponding ring slightly larger). The rings around the three largerapertures 225 are likewise larger than the ring around the smalleraperture 240. In some cases, one may be larger than the other three, ortwo may be larger or smaller than the other two, or some combinationthereof. The apertures 225/240 may alternately be referred to ascutouts, holes, voids, orifices, vents, openings, gaps, perforations,interstices, discontinuities or some combination thereof. In some cases,different types of surface discontinuities may be used instead of or inaddition to the apertures 225/240, such as raised surfaces or bumps thatcan also be detected by depth/range/distance measurement sensors such asradar or lidar.

The combined distance measurement sensor and camera calibration target250 of FIG. 2F also includes additional markings or patterns at certainedges of the substrate, identified as target identification (ID) markers235. The particular combined distance measurement sensor and cameracalibration target 250 of FIG. 2F includes target identification (ID)markers 235 on the two sides of the substrate opposite the smalleraperture/ring combination 240, but other combined distance measurementsensor and camera calibration targets 250 may have one, two, three, orfour target identification (ID) markers 235 along any of the four sidesof the square substrate, or may have target identification (ID) markers235 in an amount up to the number of sides of the polygonal substrate205 where the substrate 205 is shaped like a non-quadrilateral polygon.That is, if the substrate 205 is an octagon, each combined combineddistance measurement sensor and camera calibration target 250 may haveanywhere from zero to eight target identification (ID) markers 235.Different patterns of target identification (ID) markers 235 are furthervisible in FIG. 5A.

The substrate 205 of FIG. 2F may include or be coated with anypreviously-discussed substrate material and may be warped or shaped inany manner or include any devices discussed with respect to thesubstrate 205 of FIG. 2A, and therefore may be detected by, and beuseful to calibrate a distance measurement sensor of the vehicle, suchas a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, and may bedetected by a microphone or infrared sensor of the vehicle as well.

In some cases, the combined distance measurement sensor and cameracalibration target 250 may have more or fewer apertures andcorresponding visual markings than the four apertures and correspondingvisual markings illustrated in FIG. 2F.

In some cases, the sensor calibration target 200F illustrated in FIG. 2Fmay also function as an infrared sensor target when paired with a heatsource such as a heat lamp, an incandescent lamp, or a halogen lamp. Inparticular, the dark markings 230 around the apertures 225/240 and/orthe target ID patterns 235 may be darker than the substrate 205 andtherefore may absorb more heat than the substrate 205, and may thus bedistinguishable from the substrate 205 by an infrared sensor such as aninfrared camera.

FIG. 2G illustrates a combined sensor calibration target that ispolyhedral and includes markings on multiple surfaces.

The combined sensor calibration target 255 of FIG. 2G is polyhedral andincludes multiple surfaces. In particular, the combined sensorcalibration target 255 illustrated in FIG. 2G is cubic and this includessix surfaces, three surfaces 260A-C of which are visible from theperspective in which the combined sensor calibration target 255 isillustrated in FIG. 2G. As illustrated in FIG. 2G, a first surface 260Ais illustrated in the lower-right (front-right) portion of the combinedsensor calibration target 255, a second surface 260B is illustrated inthe lower-left (front-left) portion of the combined sensor calibrationtarget 255, and a third surface 260C is illustrated in the top portionof the combined sensor calibration target 255.

The surfaces of the polyhedral sensor calibration target 255 are markedin a pattern 210G, which is illustrated in FIG. 2G as a checkerboardpattern similar to the checkerboard pattern 210A of FIG. 2A. The pattern210G may include any combination of patterns discussed herein; forinstance, the pattern 210G may include the checkerboard pattern 210A,the ArUco pattern 210B of FIG. 2B, the crosshair pattern 210C of FIG.2C, the dot pattern 210D of FIG. 2D, the aperture 225/240 and marking230 pattern of FIG. 2F, or some combination thereof. In some cases,different surfaces may be marked with a different pattern, rather thanall of the surfaces being marked with the same pattern.

All three visible surfaces 260A-C join at a first vertex 270A. The firstsurface 260A also joins with the second surface 260B and another unseensurface at a second vertex 270B. The first surface 260A also joins withthe third surface 260C and another unseen surface at a third vertex270C. The second surface 260B also joins with the third surface 260C andanother unseen surface at a fourth vertex 270D. The first surface 260Aalso joints with two other unseen surfaces at a fifth vertex 270E. Thesecond surface 260B also joints with two other unseen surfaces at asixth vertex 270F. The third surface 260C also joints with two otherunseen surfaces at a seventh vertex 270G.

Readings from distance measurement sensors, such as light detection andranging (LIDAR) sensors, may be used to detect surfaces. For instances,readings from a LIDAR sensors may be used to generate a point cloud inwhich a location of each point relative to the location of the LIDARsensor is determined based on the distance from the LIDAR sensormeasured by the LIDAR sensor at a given point in time as well as theangle and/or orientation of the LIDAR sensor's signal that resulted inthat distance measurement. For instance, if the LIDAR sensor sends asignal north and measures a distance of 5 meters when it receives thatsignal back, then a point is marked in the point cloud as 5 meters northof the location of the LIDAR sensor. If the point cloud includes threeor more points that, connected together, form a surface, then thevehicle can infer that a surface exists there. When the polyhedralsensor target 255 is used with the LIDAR sensor with the LIDAR sensorfacing the polyhedral sensor target 255 from the perspective illustratedin FIG. 2G, the first surface 260A, the second surface 260B, and thethird surface 260C should all be identifiable from the point cloudgenerated based on the LIDAR sensor's measurements. Based purely on itsknowledge of the existence of these three surfaces 260A-C, the vehicle'sinternal computer 110 can identify the existence of the first vertex270A, the edge between the first vertex 270A and the second vertex 270B,the edge between the first vertex 270A and the third vertex 270C, andthe edge between the first vertex 270A and the fourth vertex 270D. Ifthe vehicle's internal computer 110 knows that it is pointed at thepolyhedral sensor target 255 and knows the shapes and dimensions of itssides, then the vehicle's internal computer 110 can identify the secondvertex 270B, third vertex 270C, fourth vertex 270D, fifth vertex 270E,sixth vertex 270F, and seventh vertex 270G.

Visual data (e.g., images, video) captured by one or more cameras mayalso be used to detect surfaces. In particular, if the internalcomputing device 110 of the vehicle 102 knows what pattern 210G toexpect on the surfaces of the polyhedral sensor target 255, such as thecheckerboard pattern illustrated in FIG. 2G, the visual data captured bythe camera may be used to identify the angles of the three surfaces260A-C relative to one another as well as the shapes of the sides. Thevisual data captured by the camera may be used to identify the firstvertex 270A, the second vertex 270B, the third vertex 270C, the fourthvertex 270D, the fifth vertex 270E, the sixth vertex 270F, the seventhvertex 270G, and all edges in between any two of these vertices 270. Insome cases, the visual data captured by the camera may be used toidentify the surfaces 260A-C, vertices 270A-G, and edges even if nopattern 210G is used, for instance if each of the surfaces 260A-C of thepolyhedral sensor target 255 are painted or dyed different colors, or ifshadows and/or reflects on each of the surfaces 260A-C of the polyhedralsensor target 255 make the surfaces 260A-C visually discernable, or ifthe edges and/or vertices of the polyhedral sensor target 255 arepainted/marked so as to be visually distinguishable from the surfaces260A-C, or some combination thereof.

The polyhedral sensor target 255 may be used for extrinsic calibrationbetween the camera and the LIDAR sensor (or another distance measurementsensor). In particular, one or more of the vertices 270A-G may beidentified both in a visual representation of the polyhedral sensortarget 255 that is identified within visual data (e.g., images and/orvideo) captured by the camera and in a distance measurementrepresentation of the polyhedral sensor target 255 that is identifiedwithin distance measurement data (e.g., a point cloud) captured by thedistance measurement sensor. The locations of the one or more of thevertices 270A-G as identified within these representations of thepolyhedral sensor target 255 may be mapped to the same location in thereal world (e.g., in the calibration environment that the vehicle 102 isin and that the polyhedral sensor target 255 is also in). In some cases,a transformation may be generated to map locations within the distancemeasurement representation to locations within the visual representationbased on the positions of the vertices 270A-G in these representations.

The polyhedral sensor target 255 may be used for intrinsic calibrationof the camera and/or the LIDAR sensor (or another distance measurementsensor). For instance, if the visual representation of the polyhedralsensor target 255 includes warping or distortion of the pattern 210G(e.g., due to lens shape) compared to prior knowledge of what thepattern 210G should look like, the internal computing device 110 of thevehicle 102 may generate a filter that warps or distorts images capturedby the camera to correct and/or compensate for the warping or distortionidentified. This may in some cases be more useful than a flat target asin the targets 200A-D of FIGS. 2A-D, as multiple surfaces 260A-C, edges,and corners/vertices 270A-G are visible and may all be used forcalibration.

The surfaces 260A-C (and other not visible surfaces) of the polyhedralsensor target 255 may be made of a substrate 205, which may include orbe coated with any previously-discussed substrate material and may bewarped or shaped in any manner or include any devices discussed withrespect to the substrate 205 of FIG. 2A.

In some cases, the polyhedral sensor target 255 illustrated in FIG. 2Gmay also function as an infrared sensor target when paired with a heatsource such as a heat lamp, an incandescent lamp, or a halogen lamp. Inparticular, the dark portions of the pattern 210G may be darker than thesubstrate 205 and therefore may absorb more heat than the substrate 205,and may thus be distinguishable from the substrate 205 by an infraredsensor such as an infrared camera. The infrared sensor or infraredcamera may replace the camera, or may be a third sensor to be calibratedalong with a visual camera and distance measurement sensor.

While the polyhedral sensor target 255 is illustrated as cubic in FIG.2G, other shapes may be used, such as a tetrahedron, a dodecahedron, anicosahedron, an isohedron, a deltahedron, a polycube, a zonohedron, asquare pyramid, a rectangular pyramid, a pentagonal pyramid, a pyramidwith a base that is a different polygon, a dipyramid, a triangularprism, a rectangular prism shape, a prism whose bases are a differentpolygon, an antiprism based on any of these polygons, a regularpolyhedron, a semi regular polyhedron, an irregular polyhedron, aPlatonic solid, an Archimedean solid, a Catalan solid, a Johnson solid,a stellated octahedron, another stellated polyhedron, a truncatedpolyhedron such as a truncated cube, or some combination thereof. Insome cases, the polyhedral sensor target 255 may be a polyhedral shapemade up of two or more polyhedral shapes that are intersected with oneanother or coupled to one another at one or more surfaces, such as adual tetrahedron (also known as a stellated octahedron), a compound ofcube and octahedron, a compound of dodecahedron and icosahedron, anotherdual polyhedral, or some combination thereof. In some cases, certainsurfaces 260 of the polyhedron may be missing; for instance, rather thanseeing the “exterior” of a cube or other polyhedron, the camera and/ordistance measurement sensor may see an “interior” of the cube or otherpolyhedron due to one or more exterior surface(s) being missing thatwould otherwise block a view of the interior. In such a case, thevertices 270 that are identified may be interior vertices 270 on theinterior of the polyhedral sensor target 255 in addition to or insteadof exterior vertices 270 on the exterior of the polyhedral sensor target255. Each of the surfaces 260 of the polyhedron may be flat or curved.Curved surfaces may be, for example, concave or convex or somecombination thereof.

The polyhedral sensor target 255 of FIG. 2G may in some cases benon-metallic (e.g., made from plastic, cardboard, paper, wood, foam, orsome combination thereof) so as not to be as easily detectable by aRADAR sensor, and may include a RADAR target 220 on or adjacent to thepolyhedral sensor target 255 at a known location relative to the one ormore portions of the polyhedral sensor target 255 (e.g., relative to oneor more of the vertices 270A-G). In this way, the polyhedral sensortarget 255 and RADAR target 220 may be used together to extrinsicallycalibrate the RADAR sensor along with the camera and LIDAR sensor.

Additional targets not depicted in FIG. 2A-2G may also be possible forcalibration of different types of vehicle sensors. For example, targetsfor intrinsic and/or extrinsic calibration of microphones of the vehicle102 may include speakers, buzzers, chimes, bells, or other audio outputdevices, optionally in front of, behind, or beside visual markingsand/or substrate apertures and/or lighting and/or heating elements. Insome cases, certain targets may include substrates that are backlit orfrontlit.

FIG. 3 illustrates a top-down view of a hallway calibration environmentin which a vehicle traverses a drive path along which the vehicle isflanked by vehicle sensor calibration targets.

The hallway calibration environment 300, which may also be referred toas a tunnel calibration environment, includes a thoroughfare 305 throughwhich a vehicle 102 drives, the thoroughfare 305 flanked on either sideby targets detectable by the sensors 180 of the vehicle 102. Thethoroughfare 305 may also be referred to as the drive path, the drivechannel, the hallway, or the tunnel. Some of the targets are arranged ina left target channel 310 that is to the left of the vehicle 102 as thevehicle 102 traverses the thoroughfare 305. Others of the targets arearranged in a right target channel 315 that is to the right of thevehicle 102 as the vehicle 102 traverses the thoroughfare 305. In FIG.3, the targets in the left target channel 310 and right target channel315 are all checkerboard camera targets 200A as illustrated in FIG. 2A,but they may include any other type of target discussed herein that isused to calibrate any vehicle sensor or combination of vehicle sensors,such as any of the targets of FIGS. 2A-2G. The left target channel 310and right target channel 315 may include a combination of differenttarget types, similarly to the calibration environment of FIG. 6; thetargets need not all be of a uniform type as illustrated in FIG. 3.

The vehicle 102 drives along the thoroughfare 305, stopping afterincremental amounts, for example, every foot, every N feet, every meter,or every N meters, where N is a number greater than zero, such as 1, 2,3, 4, 5, 6, 7, 8, 9, or 10. At each stop, the vehicle 102 captures datausing each of its vehicle sensors, or at least each of the vehiclesensors that it intends to calibrate. The vehicle 102 stopping helpsprevent issues caused by sensors running while the vehicle 102 is inmotion, such as motion blur or rolling shutter issues in cameras. Thevehicle 102 stopping also ensures that sensors can capture data whilethe vehicle 102 is in the same position, which may be important forextrinsic calibration of two or more sensors with respect to each otherso that a location within data gathered by a first vehicle sensor (e.g.,a distance measurement sensor such as a LIDAR or radar sensor) can beunderstood to correspond to a location within data gathered by a secondvehicle sensor (e.g., a camera). The vehicle 102 may in some casestraverse the thoroughfare 305 multiple times, for example N times ineach direction, where N is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, or 20.

The sensor targets illustrated in FIG. 3 are illustrated as each mountedon separate easel-style stands. Other types of stands are also possible,such as the type illustrated in FIG. 4, FIG. 5A, and FIG. 5B.Furthermore, multiple sensor targets, of the same type or of differenttypes may be supported by each stand (as in FIG. 4, FIG. 5A, and FIG.5B) even though this is not illustrated in FIG. 3.

The sensor targets illustrated in FIG. 3 are illustrated such that someare positioned closer to the thoroughfare 305 while some are positionedfarther from the thoroughfare 305. Additionally, while some targets inFIG. 3 are facing a direction perpendicular to the thoroughfare 305,others are angled up or down with respect to the direction perpendicularto the thoroughfare 305. While the sensor targets illustrated in FIG. 3all appear to be at the same height and all appear to not be rotatedabout an axis extending out from the surface of the target, it should beunderstood that the sensor targets may be positioned at differentheights and may be rotated about an axis extending out from the surfaceof the target as in the targets of FIGS. 4, 5A, and 5B. Together, thedistance from the thoroughfare 305, the direction faced relative to thethoroughfare 305, the clustering of targets, the height, and therotation about an axis extending out from the surface of the target mayall be varied and modified to provide better intrinsic and extrinsiccalibration. That is, these variations assist in intrinsic calibrationin that collection of data with representations of targets in variouspositions, rotations, and so forth ensures that targets are recognizedas they should be by any sensor, even in unusual positions androtations, and that any necessary corrections be performed to datacaptured by sensors after calibration. These variations assist inextrinsic calibration in that the different positions and rotations andso forth provide more interesting targets for distance measurementsensors, such as lidar, radar, sonar, or sodar, and allow distancemeasurement sensors to aid in interpretation of optical data collectedby a camera of the vehicle 102.

While the thoroughfare 305 of the hallway calibration environment 300 ofFIG. 3 is a straight path, in some cases it may be a curved path, and byextension the left target channel 310 and right target channel 315 maybe curved to follow the path of the thoroughfare 305.

While the hallway calibration environment 300 is effective in providingan environment with which to calibrate the sensors 180 of the vehicle102, it is inefficient in some ways. The hallway calibration environment300 is not space efficient, as it occupies a lot of space. Such ahallway calibration environment 300 is best set up indoors so thatlighting can be better controlled, so the hallway calibrationenvironment 300 requires a large indoor space, and by extension, a lotof light sources, which is not energy-efficient or cost-efficient.Because of how much space the hallway calibration environment 300 takesup, it is more likely to have to be taken down and set back up again,affecting consistency of calibration between different vehicles whosesensors are calibrated at different times. Further, because the setup ofthe hallway calibration environment 300 requires the vehicle 102 todrive through it, different vehicles 102 might be aligned slightlydifferently in the thoroughfare 102, and might drive a slightlydifferent path through the thoroughfare 102, and might stop at slightlydifferent spots and/or frequencies along the drive, due to manufacturingdifferences in the vehicle 102 and due to human error in setting thevehicle 102 up, all of which affects consistency of the calibration.Trying to correct for all of these potential inconsistencies, andturning the vehicle around to move it through the hallway calibrationenvironment 300 multiple times, is time and labor intensive, making thehallway calibration environment 300 time-inefficient. Additionally,because the targets are primarily to the left and right sides of thevehicle 102 hallway calibration environment 300, vehicle sensors mightnot be as well calibrated in the regions to the front and rear of thevehicle. Using a thoroughfare 305 with some turns can help alleviatethis, but again causes the hallway calibration environment 300 to takeup more space, increasing space-inefficiency.

FIG. 4 illustrates a perspective view of a dynamic scene calibrationenvironment in which a turntable that is at least partially surroundedby vehicle camera calibration targets rotates a vehicle so that thevehicle can perform intrinsic calibration of its camera sensors.

The dynamic scene calibration environment 400 of FIG. 4 includes amotorized turntable 405 with a platform 420 that rotates about a base425. In some cases, the platform 420 may be raised above thefloor/ground around the turntable 405, with the base 425 graduallyinclined up to enable the vehicle 102 to drive up the base 425 and ontothe platform 420, or to drive off of the platform 420 via the base 425.A vehicle 102 drives onto the platform 420 of the turntable 405, and themotors actuate to rotate platform 420 of the turntable 405 about thebase 425, and to thereby rotate the vehicle 102 relative to the base 425(and therefore relative to the floor upon which the base 425 rests).While the arrows on the turntable 405 show a counter-clockwise rotationof the platform 420, it should be understood that the platform 420 ofthe motorized turntable 405 can be actuated to rotate clockwise as well.The turntable 405 is at least partially surrounded by targets mounted onstands 410. In FIG. 4, the illustrated targets are allcheckerboard-patterned camera calibration targets 200A as depicted inFIG. 2A, allowing for calibration of cameras of the vehicle 102. Inother embodiments, such as in FIGS. 5A and 5B, the targets around themotorized turntable may include any other type of target discussedherein that is used to calibrate any vehicle sensor or combination ofvehicle sensors.

In one embodiment, the platform 420 of the motorized turntable 405 maybe rotated by predetermined intervals (measured in degrees/radians or anamount at a time), for example intervals of ten degrees, in betweenpoint the turntable stops so that the vehicle 102 can capture data withits sensors 180. The platform 420 of the motorized turntable 405 canstart and stop in this manner, and can eventually perform a full 360degree rotation in this manner. The motorized turntable 405 may in somecases perform multiple full 360 degree rotations in one or both rotationdirections (clockwise and counterclockwise), for example N rotations ineach rotation direction, where N is, for example, 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20.

FIG. 5A illustrates a perspective view of a dynamic scene calibrationenvironment in which a turntable that is at least partially surroundedby combined camera and LIDAR sensor calibration targets and RADAR sensorcalibration targets so that the vehicle can perform extrinsiccalibration of its camera, LIDAR, and RADAR sensors.

The dynamic scene calibration environment 500 of FIG. 5A includes thesame motorized turntable 405 as in FIG. 4. A vehicle 102 drives onto theplatform 420 of the turntable 405, and the motors actuate to rotate theplatform 420 of the turntable 405, and thereby rotate the platform 420(and the vehicle 102) about the base 425, clockwise, counter-clockwise,or one then the other. The turntable 405 is at least partiallysurrounded by targets mounted on stands 410. In FIG. 5A, the illustratedtargets include both a set of combined range/camera extrinsiccalibration targets 250 as depicted in FIG. 2F and a set of trihedralradar calibration targets 220 as depicted in FIG. 2E. Each stand 410mounts two combined range/camera extrinsic calibration targets 250 andone trihedral radar calibration targets 220, which in some cases may bea known distance from the combined range/camera extrinsic calibrationtargets 250 on the stand 410, permitting extrinsic calibration betweenthe radar and the distance measurement sensor (lidar, radar, sonar,sodar) and/or camera calibrated using the combined range/cameraextrinsic calibration targets 250.

As the vehicle 102 rotates about the base 425 on the platform 420 of themotorized turntable 405, and/or during stops between rotations, thevehicle 102 and its computer 110 can detect the combined range/cameraextrinsic calibration targets 250 using both its distance measurementsensors (e.g., lidar, etc.) and cameras by detecting the apertures 225with the distance measurement sensors and the markings 230 around theapertures and the target identifier markings 235 with the cameras. Indoing so, the vehicle 102 and its computer 110 can detect a center ofthe circular aperture 225 easily, since distance measurement sensorssuch as lidar typically provide a point cloud of depth measurements thatcan help identify where the widest pats of each circle are. The rings230 detected by the camera will also have the same centers as theapertures, so the distance measurement sensor and camera know they arelooking at the exact same locations for each of these center points.Thus, the camera and distance measurement sensor may be extrinsicallycalibrated so that their positional awareness of the surroundings of thevehicle 102 can be positionally aligned. The extrinsic calibration may,in some cases, output one or more matrices (e.g., one or moretransformation matrices) used for transforming a camera location to adistance measurement sensor location or vice versa, via translation,rotation, or other transformations in 3D space. Calibration affectsinterpretation of data captured by the sensors after calibration iscomplete. The transformation(s) that are generated during this extrinsiccalibration can include one or more types of transformations, includingtranslations, stretching, squeezing, rotations, shearing, reflections,perspective distortion, distortion, orthogonal projection, perspectiveprojection, curvature mapping, surface mapping, inversions, lineartransformations, affine transformations, The translational and rotatonaltransformations may include modifications to position, angle, roll,pitch, yaw, or combinations thereof. In some cases, specific distortionsmay be performed or undone, for example by removing distortion (e.g.,parabolic distortion) caused by use of a specific type of lens in acamera or other sensor, such as a wide-angle lens or a fisheye lens or amacro lens.

The transformation(s) generated by the computer 110 of the vehicle 102may be used for extrinsic calibration of a first sensor (e.g., thecamera) with respect to a second sensor (e.g., LIDAR or RADAR or SONARor SODAR or another distance measurement sensor), so that the computer102 can map positions identified in the data output from each sensor tothe real world environment around the vehicle 102 (e.g., in the field ofview of the sensors 180 of the vehicle 102) and relative to each other,based on known relative positions of features identified within theoutputs of each sensor. Such features may include the visual markings ofthe combined target 250 as identified by the camera, the apertures asidentified by the distance measurement sensor, and optionally atrihedral target 220 affixed near or on the target 250 as in theenvironment 500 of FIG. 5A. For example, if translation of positions indata captured by the first sensor to positions in the real world aroundthe vehicle are already clear through intrinsic calibration, buttranslation of positions in data captured by the second sensor topositions in the real world around the vehicle are already clear throughintrinsic calibration (or vice versa), then the transformation generatedthrough this extrinsic calibration can translate positions in datacaptured by the second sensor to positions in the real world around thevehicle based on (1) the position in the real world around the vehicleof the data from the first sensor, and (2) the relative positioning ofthe position in the data from the first sensor and the position in thedata from the second sensor. Thus, a sensor that has not beenintrinsically calibrated can still be calibrated extrinsically relativeto another sensor, and can still benefit from the increase in accuracygranted by the intrinsic calibration of that other sensor.

The trihedral targets 220 can also have a known distance from thecombined range/camera extrinsic calibration targets 250, and in somecases specifically from the centers of the apertures 225 and rings 230of the targets 250, allowing extrinsic calibration between the distancemeasurement sensor (e.g., radar) that recognizes the trihedral targets220 and the distance measurement sensor (e.g., lidar) that recognizesthe apertures 225 and the camera that recognizes the rings/markings 230.

In other embodiments, the targets around the motorized turntable 405 mayinclude any other type of target discussed herein that is used tocalibrate any vehicle sensor or combination of vehicle sensors, such asthe target 200A of FIG. 2A, the target 200B of FIG. 2B, the target 200Cof FIG. 2C, the target 200D of FIG. 2D, the target 220 of FIG. 2E, thetarget 250 of FIG. 2F, the target 255 of FIG. 2G, targets with heatingelements detectable by infrared sensors of the vehicle 102, targets withspeakers detectable by microphones of the vehicle 102, targets withreflective acoustic properties detectable bySONAR/SODAR/ultrasonic/infrasonic sensors of the vehicle 102, or somecombination thereof.

The stands 410 used in FIG. 3-6 may include any material discussed withrespect to the substrate 205, such as paper, cardboard, plastic, metal,foam, or some combination thereof. In some cases, certain stands may bemade of a plastic such polyvinyl chloride (PVC) to avoid detection bycertain types of distance measurement sensors, such as radar, whichdetect metal better than plastic.

In one embodiment, the platform 420 of the motorized turntable 405 maybe rotated about the base 425 by predetermined intervals (measured indegrees/radians or an amount at a time), for example intervals of tendegrees, in between point the turntable stops so that the vehicle 102can capture data with its sensors 180. The intervals may be N degrees,where N is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, or 20. The platform 420 of the motorized turntable405 can start and stop its rotation via activation and deactivation ofits motor(s) 730 in this manner, and can eventually perform a full 360degree rotation in this manner. The platform 420 of the motorizedturntable 405 may in some cases perform multiple full 360 degreerotations about the base 425 in one or both rotation directions(clockwise and counterclockwise), for example N rotations in eachrotation direction, where N is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20.

FIG. 5B illustrates a perspective view of a dynamic scene calibrationenvironment in which a turntable that is at least partially surroundedby combined polyhedral sensor calibration targets so that the vehiclecan perform extrinsic calibration of its camera, LIDAR, and RADARsensors.

The dynamic scene calibration environment 550 of FIG. 5B includes thesame motorized turntable 405 as in FIGS. 4 and 5A, at least partiallysurrounded again by sensor calibration targets on stands 410. In thedynamic scene calibration environment 550 of FIG. 5B, the stand 410 iscoupled to one polyhedral combined sensor target 255 (as in FIG. 2G) andone RADAR target 220 (as in FIG. 2E). Each of the RADAR targets 220 maybe a known distance from the polyhedral combined sensor target 255 onthe same stand 410 so that the RADAR target 220 may be used forextrinsic calibration of the RADAR sensor with the camera and LIDARsensor. In other cases, a stand may be coupled to fewer (e.g., zero) ormore (e.g., two, three, four, five, or more) polyhedral combined sensortarget 255 and/or RADAR targets 220. The sensor targets 255/220 enablethe vehicle to intrinsically and/or extrinsically calibrate the camera,the RADAR sensor, the LIDAR sensor, or some combination thereof.

The transformation(s) generated by the computer 110 of the vehicle 102may be used for extrinsic calibration of a first sensor (e.g., camera,IR camera, LIDAR, RADAR, SONAR, SODAR) with respect to a second sensor(e.g., camera, IR camera, LIDAR, RADAR, SONAR, SODAR), so that thecomputer 102 can map positions identified in the data output from eachsensor to the real world environment around the vehicle 102 (e.g., inthe field of view of the sensors 180 of the vehicle 102) and relative toeach other, based on known relative positions of features identifiedwithin the outputs of each sensor. Such features may include vertices270 of a polyhedral sensor target 255 as identified by both camera anddistance measurement sensor. For example, if translation of positions indata captured by the first sensor to positions in the real world aroundthe vehicle are already clear through intrinsic calibration, buttranslation of positions in data captured by the second sensor topositions in the real world around the vehicle are already clear throughintrinsic calibration (or vice versa), then the transformation generatedthrough this extrinsic calibration can translate positions in datacaptured by the second sensor to positions in the real world around thevehicle based on (1) the position in the real world around the vehicleof the data from the first sensor, and (2) the relative positioning ofthe position in the data from the first sensor and the position in thedata from the second sensor. Thus, a sensor that has not beenintrinsically calibrated can still be calibrated extrinsically relativeto another sensor, and can still benefit from the increase in accuracygranted by the intrinsic calibration of that other sensor.

FIG. 6 illustrates a top-down view of a dynamic scene calibrationenvironment in which a turntable that is at least partially surroundedby various types of vehicle sensor calibration targets.

The dynamic scene calibration environment 600 of FIG. 6 is, in someways, of a combination of the dynamic scene calibration environment 400of FIG. 4, the dynamic scene calibration environment 500 of FIG. 5A, andthe dynamic scene calibration environment 550 of FIG. 5B, in thatmultiple types of targets from all of these figures are positionedaround the motorized turntable 405. These three types of targets includethe checkerboard-patterned camera calibration targets 200A of FIG. 2Athat are used in the dynamic scene calibration environment 400 of FIG.4, the trihedral radar calibration targets 220 of FIG. 2E, the combinedsensor targets 250 of FIG. 2F that are used in the dynamic scenecalibration environment 500 of FIG. 5A, and the combined sensor targets255 of FIG. 2G that are used in the dynamic scene calibrationenvironment 550 of FIG. 5B. The dynamic scene calibration environment600 of FIG. 6 also includes light sources 620 and/or heat sources 625.Some or all of the heat sources 625 may, in some cases, also be lightsources 620, and vice versa. Alternately, independent light sources 620and heat sources 625 may be used.

Any of the stands used in FIG. 3-6 may be any type of stands, such aseasel-type stands, tripod-type stands, or the rod stands 410 with widebases seen in FIG. 4 and FIG. 5A and FIG. 5B. In some cases, the stands410 may be coupled to mobile robots, which may form the base of thestand 410, and which may be used to move the stand into position fromstorage and/or to make adjustments as needed and/or back into storage.Any of the stands used in FIG. 3-6 may include paper, cardboard,plastic, metal, foam, or some combination thereof as previouslydiscussed. Some stands 410 may be non-metallic and intentionally avoidusing metal so that the stand does not interfere with the RADAR sensorof the vehicle 102. The stands in some cases may includeremote-controllable motors and actuators enabling positions and/orangles of rotation of targets to be manipulated, for example to be moreclearly visible given lighting supplied by light sources 620, to receivemore heat given heating supplied by heat sources 625, and/or to access aregion of a point of view of a particular vehicle sensor from which thatvehicle sensor has not captured enough data and therefore around whichthe vehicle sensor is under-calibrated. In some cases, these motors andtheir actuators may be controlled wirelessly by the vehicle 102 and/orscene surveying system 610 as discussed further with respect to FIG. 7and FIG. 14A-B.

The dynamic scene calibration environment 600 of FIG. 6 also includes ascene surveying system 610, which may include one or more visualcameras, IR cameras (or other IR sensors), one or more distancemeasurement sensors (e.g., radar, lidar, sonar, sodar, laserrangefinder), any other sensor discussed as being a vehicle sensor, orsome combination thereof. This may take the form of a robotic totalstation (RTS). While the vehicle 102, its sensors 180, and the targetsare enough to perform intrinsic calibration of sensors to correct fordistortions, for example, and to perform extrinsic calibration ofsensors to align locations within data captured by different sensors,for example, in some cases more information may be needed to understandhow far these locations within data captured by different sensors arefrom the vehicle itself. The scene surveying system 610 captures visualand/or range data of at least a subset of the dynamic scene calibrationenvironment 600, including the motorized turntable 410, at least some ofthe targets, and the vehicle 102 itself. Key points on the vehicle 102may be tracked to identify the current pose (rotationorientation/position) of the vehicle 102 (and therefore of the platform420 about the base 425). Data captured by the scene surveying system 610can also be sent to the vehicle 102 and used to verify the data capturedby the sensors and the intrinsic and extrinsic calibrations performedbased on this data. The dynamic scene calibration environment 600 alsoincludes several light sources 620 set up around the turntable 405.These light sources 620 may include any type of light source, such asincandescent bulbs, halogen bulbs, light emitting diodes (LEDs), and soforth. In some cases, the light sources 620 may include stage lights orspotlights. Mirrors and/or lenses may be used to manipulate light fromthe light sources 620 to relatively evenly illuminate the targets and/orthe dynamic scene calibration environment 600 as a whole. Additionallight sources may be positioned over the turntable 405 or behind some orall targets, especially if targets use a transparent or translucentsubstrate. The light sources 620 are used to improve readings fromcameras of the vehicle and of the scene surveying system 610, and can insome cases be controlled wirelessly by the vehicle 102 and/or scenesurveying system 610 as discussed further with respect to FIG. 7 andFIG. 14A-B. The light sources 620 may in some cases also function asheat sources 625 for sensor calibration targets for calibrating IRsensors.

By including targets for intrinsic sensor calibration as well as targetsfor extrinsic calibration around the turntable 405, the sensors 180 ofthe vehicle 102 can receive a comprehensive calibration in the dynamicscene calibration environment 600. The dynamic scene calibrationenvironments 400, 500, 550, and 600 of FIGS. 4-6 have several advantagesover the static hallway calibration environment 300 of FIG. 3. First,the dynamic scene calibration environment is more space-efficient, as itonly requires space for the vehicle turntable 405, space for sometargets, light sources 620 and/or heat sources 625, and in some cases ascene surveying system 610. There is no need to clear space for athoroughfare 305 or other long path. Because fewer light sources 620 areneeded to light a smaller space like the dynamic scene calibrationenvironment 600 than a larger space like the hallway calibrationenvironment 300, the dynamic scene calibration environment 600 is moreenergy-efficient in terms of lighting. Because the vehicle engine can beturned off after the vehicle is on the turntable 405, the dynamic scenecalibration environment 600 is more energy-efficient in terms ofvehicular power usage. Because changing rotation directions isconsiderably quicker than performing a U-turn in a vehicle to changedriving directions, and because a smaller space is more likely to remainuntouched and therefore won't need take-down and setup after and beforeeach use, the dynamic scene calibration environment 600 is moretime-efficient. Because the target and lighting setup, and the movementof the vehicle 102, can be better controlled, the calibration resultsare more consistent across vehicles 102, which further allowscalibration configurations of different vehicles 102 to be more readilycompared and outliers identified as potentially symptomatic of a sensordefect or a miscalibration issue. Presence of guide railings (not shown)along the platform of the turntable may further increase consistency ofcalibration by increasing consistency of positioning of the vehicle 102along the platform of the turntable 405. The dynamic scene calibrationenvironment 600 also allows the sensors 180 of the vehicle 102 tocapture data all around the vehicle 102—including in the front and rearof the vehicle 102, which receive less data in the hallway calibrationenvironment 300.

FIG. 7 illustrates a system architecture of a dynamic scene calibrationenvironment.

The system architecture 700 of the dynamic scene calibration environmentof FIG. 7 includes a number of main elements, each with sub-components.The main elements include the autonomous vehicle 102, a dynamic scenecontrol bridge 710, the motorized turntable system 450, alighting/heating system 760, a target control system 770, a scenesurveying system 610, a robotic scene control system 790, and a powersupply system 702.

The autonomous vehicle 102 includes the one or more sensors 180, the oneor more internal computing devices 110, one or more wirelesstransceivers 705 (integrated with communication service 116), and anyother elements illustrated in and discussed with respect to FIG. 1. Thesensors 180 may in some cases include one or more GPRS receivers,Bluetooth® beacon-based positioning receivers, an inertial measurementunit (IMU), one or more cameras, one or more lidar sensors, one or moreradar sensors, one or more sonar sensors, one or more sodar sensors,and/or one or more other sensors discussed herein, which may in somecases be used to identify when the vehicle 102 is not on the platform420, when the vehicle 102 is on the platform 420, when the vehicle 102in a defined position on the platform (e.g., optionally as guided byguide railings on the platform), when the vehicle 102 have begunrotating from a stopped position, and/or when the platform 420 and/orthe vehicle 102 have stopped rotating. The computing device 110 of thevehicle 102 (or its sensors 180) may then automatically communicate oneor more signals or messages through wired and/or wireless communicationinterfaces, for instance through the wireless transceiver 705, to thebridge 710, the computing device 110 of the vehicle 102 (if thecommunication is straight from the sensors 180), and/or the computingdevice 745 of the motorized turntable system 405 to convey any of theseobservations/detections/identifications by the sensors 180, which may beused to trigger various actions, such as rotation or stop of rotation ofthe turntable 405, collection of sensor data or stop of collection ofsensor data at the vehicle 102, or some combination thereof.

The dynamic scene control bridge 710 includes one or more computingdevices 715 and one or more wired and/or wireless transceivers 720. Thedynamic scene control bridge 710 is optional, but can serve as a“middleman” or “router” between the autonomous vehicle 102 and theremaining main elements of the system architecture 700, such as thedynamic scene control bridge 710, the motorized turntable system 450,the lighting/heating system 760, the target control system 770, thescene surveying system 610, and the power supply system 702. The dynamicscene control bridge 710 can in some cases convert file formats, performmathematical operations such as operation conversions, or otherwiseinterpret instructions or data as necessary so that the vehicle 102 cansuccessfully communicate with other elements. In fact, the dynamic scenecontrol bridge 710 can perform similar conversions, mathematicaloperations, or interpretations in its role as a middleman between anytwo or more devices of the architecture 700.

The motorized turntable system 405 includes a turntable structure 725 aswell as one or more motors, encoders, actuators, and/or gearboxes 730for actuating rotation of the turntable structure 725 while the vehicle102 is on it. The motorized turntable 725 may include a platform 420,which is a surface upon which the vehicle 102 rests during calibration.The platform 420 is rotatable about a base 425 of the motorizedturntable structure 725, with one or more motors 730 that, when actuatedor activated, rotate the platform 420 about the base 425, and which stopthe rotation of the platform 420 about the base 425 when the motors 730are deactivated. For example, the one or more motors 730 may rotate theplatform 420 about the base 425 from a first rotational orientation to asecond rotational orientation, or alternately back to the firstrotational orientation (e.g., if the rotation is a 360 degree rotationor a multiple thereof). A rotational orientation of the platform 420relative to the base 425, or of the vehicle 102 relative to the base425, may alternately be referred to as a rotational position. Themotorized turntable system 405 may include one or more sensors 735, suchas pressure sensors, for example to identify whether or not the vehicle102 is on the turntable structure 725, whether or not the vehicle 102 ispositioned correctly on the turntable structure 725, or how thevehicle's weight is distributed generally or across the platform 420'stop surface (which is in contact with the wheels and/or tires vehicle102) of the turntable structure 725. The sensors 735 may in some casesidentify when the platform 420 has no vehicle 102 on it, when thevehicle 102 is on the platform 420, when the vehicle 102 in a definedposition on the platform (e.g., optionally as guided by guide railingson the platform), when the platform 420 and/or the vehicle 102 havebegun rotating from a stopped position, and/or when the platform 420and/or the vehicle 102 have stopped rotating. The motorized turntablesystem 405 may then automatically communicate one or more signals ormessages through wired and/or wireless communication interfaces to thebridge 710, vehicle 102, and/or the computing device 745 of themotorized turntable system 405 to convey any of theseobservations/detections/identifications by the sensors 735, which may beused to trigger various actions, such as rotation or stop of rotation ofthe turntable 405, collection of sensor data or stop of collection ofsensor data at the vehicle 102, or some combination thereof. Thecontroller 740 may be used to control the actuation of the motors,encoders, actuators, and/or gearbox(es) 730, for example to control aspeed or rate or angular velocity of rotation, an angular acceleration(or deceleration) of rotation, a direction of rotation (e.g., clockwiseor counterclockwise), or some combination thereof. The motorizedturntable system 405 includes one or more computing devices 745 and oneor more wired and/or wireless transceivers 750, through which it mayinteract with the vehicle 102, the dynamic scene control bridge 710, orany other element in the architecture 700.

The lighting/heating system 760 includes one or more light sources 620and/or one or more heat sources 625. The lighting/heating system 760includes one or more motors and/or actuators 762 for activating orturning on each of the light sources 620 and/or heat sources 625,disabling or turning off each of the light sources 620 and/or heatsources 625, fading or dimming each of the light sources 620 and/or heatsources 625, brightening each of the light sources 620 and/or heatsources 625, increasing or reducing heat output of each of the lightsources 620 and/or heat sources 625, moving each of the light sources620 with an actuated motor (e.g., to output light and/or heat toward aparticular target), or some combination thereof. The lighting/heatingsystem 760 includes one or more computing devices 764 and one or morewired and/or wireless transceivers 766, through which it may interactwith the vehicle 102, the dynamic scene control bridge 710, or any otherelement in the architecture 700.

The target control system 770 includes one or more targets and targetsupport structure 772. The targets may include one or more of any of thetargets 200A, 200B, 200C, 200D, 220, 250, and/or 255, illustrated inFIG. 2A-2G, any other target described herein, any other sensorcalibration target, or some combination thereof. The target supportstructures may include easel-type support structures, tripod-typesupport structures, or the rod-type support structures 410 with widebases seen in FIG. 4 and FIG. 5A and FIG. 5B. The target supportstructures may include any material discussed with respect to thesubstrate 205, such as paper, cardboard, plastic, metal, foam, or somecombination thereof. In some cases, certain stands may be made of aplastic such polyvinyl chloride (PVC) to avoid detection by certaintypes of distance measurement sensors, such as radar, which detect metalbetter than plastic.

The targets and/or support structures 720 may in some cases bemotorized, and as such, the target control system 770 may include motorsand actuators 774 that it can use to move the targets, for example asrequested by the vehicle 102 to optimize calibration. For example, thetarget support structures may include a robotic arm with ball jointsand/or hinge joints that may be actuated using the motors and actuators774 to translate a target in 3D space and/or to rotate a target aboutany axis. The motors and actuators 773 may alternately only control asingle type of movement for a particular target, for example by enablinga target to rotate about the rod of a stand 410. The target supportstructure 772 may also include wheels or legs, which may be actuated bythe motors 774 to enable the entire target support structure 772 tomove, and with it, the target(s) it supports. The target control system770 includes one or more computing devices 776 and one or more wiredand/or wireless transceivers 778, through which it may interact with thevehicle 102, the dynamic scene control bridge 710, or any other elementin the architecture 700.

The scene surveying system 610 includes a surveying device supportstructure 780, such as a tripod or any other structure discussed withrespect to the target support structure 772, and one or more sensors 782coupled to the support structure 780. The sensors 782 of the scenesurveying system 610, like the sensors 180 of the vehicle 102, mayinclude one or more cameras of any type (e.g., wide-angle lens, fisheyelens), one or more distance measurement sensors (e.g., radar, lidar,emdar, laser rangefinder, sonar, sodar), one or more infrared sensors,one or more microphones, or some combination thereof. Using these, thescene surveying system 610 can capture a representation of the entiredynamic scene, including the vehicle 102, allowing determination ofdistances between the vehicle 102 and various targets. In some cases,either the vehicle 102 or the scene surveying system 610 or both mayrequest adjustment of lighting and/or heating through thelighting/heating system 760 and/or adjustment of target positioning viathe target control system 770. The scene surveying system 610 includesone or more computing devices 784 and one or more wired and/or wirelesstransceivers 786, through which it may interact with the vehicle 102,the dynamic scene control bridge 710, or any other element in thearchitecture 700. In some cases, feature tracking and/or imagerecognition techniques applied using the computing device 784 may beused with the a camera and/or the radar, lidar, sonar, sodar, laserrangefinder, and/or other sensors 782 of the scene surveying system 610to identify when the platform 420 has no vehicle 102 on it, when thevehicle 102 is on the platform 420, when the vehicle 102 in a definedposition on the platform (e.g., optionally as guided by guide railingson the platform), when the platform 420 and/or the vehicle 102 havebegun rotating from a stopped position, and/or when the platform 420and/or the vehicle 102 have stopped rotating. The scene surveying system610 may then automatically communicate one or more signals or messagesthrough wired and/or wireless communication interfaces to the bridge710, vehicle 102, and/or motorized turntable system 405 to convey any ofthese observations/detections/identifications by the scene surveyingsystem 610, which may be used to trigger various actions, such asrotation or stop of rotation of the turntable 405, collection of sensordata or stop of collection of sensor data at the vehicle 102, or somecombination thereof. In some cases, the scene surveying system 610 maysimply be referred to as a camera or as another sensor that the scenesurveying system 610 includes.

The robotic scene control system 790 includes one or more mobile robots788. The mobile robots 788 may be coupled to one or more sensor targets,illuminated or otherwise, and/or to one or more light sources. Themobile robots 788 may include wheels and may drive the one or moresensor targets and/or one or more light sources into positions aroundthe turntable 405, for example within a predetermined radius of theturntable 405.

The mobile robots 788 may include various components. These componentsmay include motors, actuators, and telescopic and/or screw liftingmechanisms 792. These may be used to control the vertical height atwhich the one or more sensor targets and/or one or more light sourcesthat are coupled to each of the mobile robots 788 are positioned at. Thecomponents may include sensors 794. The sensors 794 may include anycombination of the sensors 830 discussed with respect to the sensors 180of the vehicle, the sensors 782 of the scene surveying system 610, orsome combination thereof. The components may include one or morecontrollers 795 and/or computing devices 796 inside the mobile robots788 and/or exterior to the mobile robots 788, which may be used to guidethe mobile robots 788 autonomously. The components may include mayfurther include one or more wireless transceivers 798, through which themobile robots 788 may interact with one another, with the vehicle 102,the dynamic scene control bridge 710, or any other element in thearchitecture 700.

The power supply system 702 may include batteries, generators, or mayplug into an outlet and into the power grid. The power supply system 702may supply power to the various elements and components of the systemarchitecture 700, including at least the dynamic scene control bridge710, the motorized turntable system 450, the lighting/heating system760, the target control system 770, and the scene surveying system 610.The power supply system 702 may also charge the vehicle 102 before,during, and/or after calibration, for example if the vehicle 102 iselectric or hybrid. The power supply system 702 may also chargebatteries of mobile robots 788 and/or power other systems of the roboticscene control system 790. The power supply system 702 may alsointelligently scale voltage, amperage, and current as appropriate foreach element and component of the system architecture 700, and to do soit may include a computing system 1500. It may also include a wiredand/or wireless transceiver (not pictured) through which it may interactwith the vehicle 102, the dynamic scene control bridge 710, or any otherelement in the architecture 700.

The computing devices 110, 715, 745, 764, 776, and 784 may each, atleast in some cases, include at least one computing system 1500 asillustrated in or discussed with respect to FIG. 15, or may include atleast a subset of the components illustrated in FIG. 15 or discussedwith respect to computing system 1500.

FIG. 8 illustrates vehicle operations for sensor calibration for atleast a camera and a distance measurement sensor.

In particular, FIG. 8 illustrates a process 800, which may be performedby a computing device coupled to one or more sensors. In some cases, theprocess 800 may be performed by an internal computing device 110 of avehicle 102 with sensors 180.

At step 805, a plurality of camera datasets are received from a cameracoupled to a housing. The housing moves between a plurality of positionsover the course of a calibration time period. The camera captures theplurality of camera datasets over the course of the calibration timeperiod. Each of the plurality of positions corresponds to at least oneof the plurality of camera datasets. The housing may, in some cases, bea vehicle 102, such as an automobile. The plurality of positions may bereached by rotation of the housing (e.g., the vehicle 102) while thehousing is atop the platform 420 of a motorized turntable 405 and whilethe platform 420 rotates about the base 425 of the motorized turntable405.

Step 810 includes identifying, within at least a subset of the pluralityof camera datasets, a visual representation of a polyhedral sensortarget 255. A plurality of surfaces of the polyhedral sensor target 255are visually discernable from one another in the visual representationof the polyhedral sensor target 255. The polyhedral sensor target 255may in some cases include markings 210G, such as checkerboard patternedmarkings or any other combination of different types of patterns 210A-Hillustrated herein or otherwise discussed herein, which may be used tomake the surfaces of the polyhedral sensor target visually discernablefrom one another in the visual representation of the polyhedral sensortarget 255.

At step 815, a plurality of distance measurement datasets are receivedfrom a distance measurement sensor coupled to the housing. The distancemeasurement sensor captures the plurality of distance measurementdatasets over the course of the calibration time period. Each of theplurality of positions corresponds to at least one of the plurality ofdistance measurement datasets. In some cases, the distance measurementsensor may be a LIDAR sensor, a RADAR sensor, a SONAR sensor, a SODARsensor, an EmDAR sensor, a laser rangefinder, or some combinationthereof.

Step 820 includes identifying, within at least a subset of the pluralityof distance measurement datasets, a distance measurement representationof the polyhedral sensor target 255. The plurality of surfaces of thepolyhedral sensor target 255 are discernable from one another in thedistance measurement representation of the polyhedral sensor target 255,for example as sets of points in a point cloud that all lie within thesame planar region.

Step 825 includes calibrating interpretation of data captured by thecamera and by the distance measurement sensor based on the visualrepresentation of the polyhedral sensor target 255 and the distancemeasurement representation of the polyhedral sensor target 255. In somecases, calibrating interpretation of the data captured by the camera andby the distance measurement sensor includes identifying a visualrepresentation of a vertex of the polyhedral sensor target 255 in thevisual representation of the polyhedral sensor target 255, identifying adistance measurement representation of the vertex of the polyhedralsensor target 255 in the distance measurement representation of thepolyhedral sensor target 255, and mapping the visual representation ofthe vertex and the distance measurement representation of the vertex toa single (same) location in a calibration environment in which thehousing and the polyhedral sensor target 255 are within (i.e., in thereal world). In some cases, calibrating interpretation of the datacaptured by the camera and by the distance measurement sensor includesgenerating a transformation that maps between one or more positions ofthe one or more markings in the visual representation of the polyhedralsensor target 255 and one or more positions of the one or more markingsin the distance measurement representation of the polyhedral sensortarget 255. In some cases, calibrating interpretation of the datacaptured by the camera and by the distance measurement sensor includesgenerating a transformation that maps between one or more portions ofthe visual representation of the polyhedral sensor target 255 and one ormore positions associated with the polyhedral sensor target 255 in acalibration environment. In some cases, calibrating interpretation ofthe data captured by the camera and by the distance measurement sensorincludes generating a transformation that maps between one or moreportions of the distance measurement representation of the polyhedralsensor target 255 and one or more positions associated with thepolyhedral sensor target 255 in a calibration environment.

In some cases, the polyhedral sensor target 255 may be attached to amotor and rotated so that data corresponding to different surfaces maybe captured/measured by the sensors, or so that data corresponding tothe same surfaces at different angles can be captured/measured by thesensors, or some combination thereof.

FIG. 9 illustrates vehicle operations for sensor calibration for asecondary distance measurement sensor in addition to the camera anddistance measurement sensor of FIG. 8.

In particular, FIG. 9 illustrates a process 900, which may be performedby a computing device coupled to one or more sensors. In some cases, theprocess 900 may be performed by an internal computing device 110 of avehicle 102 with sensors 180.

At step 905, a plurality of secondary distance measurement datasets arereceived from a secondary distance measurement sensor coupled to thehousing. The secondary distance measurement sensor captures theplurality of secondary distance measurement datasets over the course ofthe calibration time period. Each of the plurality of positionscorresponds to at least one of the plurality of secondary distancemeasurement datasets. The secondary distance measurement sensor may, insome cases, be a different type of sensor than the distance measurementsensor. For instance, the distance measurement sensor may be a LIDARsensor, while the secondary distance measurement sensor is a RADARsensor, or vice versa.

Step 910 includes identifying, within at least a subset of the pluralityof secondary distance measurement datasets, a secondary distancemeasurement representation of a secondary sensor target with a knownrelative location to the polyhedral sensor target 255. For instance, ifthe secondary distance measurement sensor is a RADAR sensor, then thesecondary sensor target may be a RADAR target 220 positioned a knowndistance and orientation relative to the polyhedral sensor target 255 asin FIG. 5B.

Step 915 includes calibrating interpretation of data captured by thecamera and by the distance measurement sensor and by the secondarydistance measurement sensor based on the visual representation of thepolyhedral sensor target, the distance measurement representation of thepolyhedral sensor target, and the secondary distance measurementrepresentation of the secondary sensor target.

FIG. 10 is a flow diagram illustrating operation of a calibrationenvironment. In particular, FIG. 10 illustrates a process 1000.

At step 1005, a high resolution map of calibration environment isgenerated. This may be performed using the scene surveying system 610,for example.

At step 1010, all sensors 180 on the vehicle 102 are run in thecalibration environment, for example at different rotation positions ofthe vehicle 102, which is rotated using motorized turntable 405. At step1015, the vehicle 102 generates a calibration scene based on its sensors180, based on (a) synchronized sensor data, (b) initial calibrationinformation, (c) vehicle pose information, and (d) target locations.

At step 1015, the calibration systems in the vehicle read thecalibration scene and: (a) detect targets in each sensor frame, (b)associate detected targets, (c) generate residuals, (d) solvecalibration optimization problem, (e) validate calibration optimizationsolution, and (f) output calibration results. At step 1025, thecalibration results are tested against acceptable bounds and checked fornumerical sensitivity. Successful calibration measurements are storedand logged, along with a minimal subset of data needed to reproducethem.

FIG. 11 is a flow diagram illustrating operations for intrinsiccalibration of a vehicle sensor using a dynamic scene. In particular,FIG. 11 illustrates a process 1100.

At step 1105, a vehicle 102 is rotated into a plurality of vehiclepositions over a course of a calibration time period using a motorizedturntable 405. The vehicle 102 and motorized turntable 405 are locatedin a calibration environment. At step 1110, the vehicle 102 captures aplurality of sensor capture datasets via a sensor coupled to the vehicleover the course of the calibration time period by capturing at least oneof the plurality of sensor capture datasets while the vehicle is at eachof the plurality of vehicle positions.

At step 1115, an internal computing system 110 of the vehicle 102receives the plurality of sensor capture datasets from the sensorcoupled to the vehicle over a course of a calibration time period. Atstep 1120, the internal computing system 110 of the vehicle 102identifies, in the plurality of sensor capture datasets, one or morerepresentations of (at least portions of) the calibration environmentthat include representations of a plurality of sensor targets. Theplurality of sensor targets are located at known (i.e., previouslystored) positions in the calibration environment. At steps 1125-1130,the sensor is calibrated based on the representations of a plurality ofsensor targets identified in the plurality of sensor capture datasets.

More specifically, at step 1125, the internal computing system 110 ofthe vehicle 102 identifies positions of the representations of theplurality of sensor targets within the one or more representations of(at least portions of) the calibration environment. If the sensor beingcalibrated is a visual light or infrared camera, and the one or morerepresentations of (portions of) the calibration environment are images,then the representations of the sensor targets may be areas within theone or more images comprised of multiple pixels, which the computingsystem 110 of the vehicle 102 can identify within the one or more imagesby generating high-contrast versions of the one or more images (i.e.,“edge” images) that are optionally filtered to emphasize edges withinthe image, and by identifying features within the image comprised of oneor more of those edges, the features recognizable as portions of thetarget. For example, the vertices and/or boxes in the checkerboardpattern 210A or the ArUco pattern 210B or pattern 210G, curves orvertices in the crosshair pattern 210C, the circular ring markingpatterns 230 or dot patterns 210D, or combinations thereof, may each bevisually recognized as features in this way. If the camera is aninfrared camera, these edges may correspond to edges betweenhotter/warmer areas (that have absorbed more heat) and cooler/colderareas (that have absorbed less heat). The hotter areas may be, forexample, dark markings after absorbing heat from a heat source 625,while the colder areas may be the substrate 205, which does not absorbas much heat from the heat source 625.

Similarly, if the sensor being calibrated is a radar sensor, the radarsensor may recognize the trihedral shape 215 of the target 220 as afeature due to its reflective pattern that results in a high radar crosssection (RCS) return. Similarly, if the sensor being calibrated is alidar sensor, the lidar sensor may recognize the surface of thesubstrate 205 of the target 250 and the apertures 225/240 within thesubstrate 205 of the target 250, which may be recognized as a featuredue to the sharp changes in range/depth at the aperture.

At step 1130, the internal computing system 110 of the vehicle 102generates a transformation that maps (1) the positions of therepresentations of the plurality of sensor targets within one or morerepresentations of (portions of) the calibration environment to (2) theknown positions of the plurality of sensor targets within thecalibration environment. Other information about the plurality of sensortargets, such as information storing visual patterns or aperturepatterns of the sensor targets, may also be used to generate thetransformation. For example, if the sensor being calibrated is a camera,and the computing device 110 knows that an image should have acheckerboard pattern 210A of a sensor target 200A, and recognizes awarped or distorted variant of the checkerboard pattern 210A (e.g.,because the camera includes a fisheye lens or wide-angle lens), then thecomputing device 110 may use its knowledge of the way that thecheckerboard should look, such as how far the vertices are from eachother, that they should form squares, and that the squares are arrangedin a grid pattern—to generate a transformation that undoes thedistortion caused by the camera, thereby mapping the vertices detectedin the image to real-world positions, at least relative to one another.In other words, the transformation includes one or more projectivetransformations of various 2-D image coordinates of sensor targetfeatures into 3-D coordinates in the real world and optionally back into2-D image coordinates that have been corrected to remove distortionand/or other sensor issues.

Because the computing device 110 knows ahead of time exactly where thesensor targets are in the calibration environment, the transformationmay also map the positions of the vertices in the image (and thereforethe positions of the representations of the sensor targets in therepresentation of the calibration environment) to real-world positionsin the calibration environment. The transformation(s) that are generatedduring intrinsic sensor calibration at step 1130 can include one or moretypes of transformations, including translations, stretching, squeezing,rotations, shearing, reflections, perspective distortion, distortion,orthogonal projection, perspective projection, curvature mapping,surface mapping, inversions, linear transformations, affinetransformations, The translational and rotatonal transformations mayinclude modifications to position, angle, roll, pitch, yaw, orcombinations thereof. In some cases, specific distortions may beperformed or undone, for example by removing distortion caused by use ofa specific type of lens in a camera or other sensor, such as awide-angle lens or a fisheye lens or a macro lens.

Step 1130 may be followed by step 1105 and/or by step 1110 ifcalibration is not yet complete, leading to gathering of more sensorcapture datasets and further refinement of the transformation generatedat step 1130. Step 1130 may alternately be followed by step 1145.

The previously stored information about the plurality of sensor targetsmay be from a high-definition map generated as in step 1005 of FIG. 10,may be from a second sensor on the vehicle, or may simply be based on aprior understanding of the sensor targets. For example, the internalcomputing system 110 of the vehicle 102 understands what a checkerboardpattern 210A is and that the grid it forms ought to look include a gridof parallel and perpendicular lines under normal conditions. Because ofthis, the internal computing system 110 understands that if therepresentation it received from the sensor (camera) of a target with acheckerboard pattern 210A forms a grid warped or distorted by awide-angle lens or fisheye lens, this difference (step 1130) can becorrected by the internal computing system 110 by correctivelydistorting or warping the image captured by the sensor (camera) by toreverse the warping or distortion in the representation until thecorrected checkerboard looks like it should. This corrective warping ordistortion is the correction generated in step 1135. The correction mayalso include a translation along X, Y, or Z dimensions, a rotation alongany axis, a warp or distortion filter, a different type of filter, orsome combination thereof.

Steps 1145-1160 concern operations that occur after calibration iscomplete (i.e., post-calibration operations). At step 1145, the sensorof the vehicle captures a post-calibration sensor capture dataset afterthe calibration time period, after generating the transformation, andwhile the vehicle is in a second position that is not in the calibrationenvironment. At step 1150, the computing device 110 of the vehicle 102identifies a representation of an object within a representation of ascene identified within the post-calibration sensor capture dataset. Atstep 1155, the computing device 110 of the vehicle 102 identifies aposition of the representation of the object within the representationof the scene. At step 1160, the computing device 110 of the vehicle 102identifies a position of the object relative to the second position ofthe vehicle by applying the transformation to the position of therepresentation of the object within the representation of the scene.

Note that capture of data by the sensors 180 of the vehicle 102 mayoccur in parallel with calibration of the sensors 180 of the vehicle102. While an initial correction is generated at step 1135, the vehicle102 may continue to rotate, and its sensors 180 may continue to capturemore sensor data, hence the dashed lines extending back up to steps 1105and 1110 from step 1135. When step 1135 is reached a second, third, orN^(th) time (where N is any integer over 1), the correction generatedthe first time step 1135 was reached may be updated, revised, and/orre-generated based on the newly captured sensor data when step 1135 isreached again. Thus, the correction becomes more accurate as calibrationcontinues.

For some additional context on intrinsic calibration: LIDAR intrinsicproperties may include elevation, azimuth, and intensity. Cameraintrinsic properties may be given as matrices based on cameraregion/bin, and may track projection, distortion, and rectification. Allsensors' intrinsic properties (including LIDAR and camera) may includeposition in X, Y, and/or Z dimensions, as well as roll, pitch, and/oryaw.

FIG. 12 is a flow diagram illustrating operations for extrinsiccalibration of two sensors in relation to each other using a dynamicscene. In particular, FIG. 12 illustrates a process 1200.

At step 1205, a vehicle 102 is rotated into a plurality of vehiclepositions over a course of a calibration time period using a motorizedturntable 405. At step 1210, the vehicle 102 captures a first pluralityof sensor capture datasets via a first sensor coupled to the vehicleover the course of the calibration time period by capturing at least oneof the first plurality of sensor capture datasets while the vehicle isat each of the plurality of vehicle positions. At step 1215, the vehicle102 captures a second plurality of sensor capture datasets via a secondsensor coupled to the vehicle over the course of the calibration timeperiod by capturing at least one of the first plurality of sensorcapture datasets while the vehicle is at each of the plurality ofvehicle positions. Either of steps 1210 and 1215 can occur first, orthey can occur at least partially in parallel.

At step 1220, the internal computing system 110 of the vehicle 102receives the first plurality of sensor capture datasets from the firstsensor and the second plurality of sensor capture datasets from thesecond sensor. At step 1225, the internal computing system 110 of thevehicle 102 identifies, in the first plurality of sensor capturedatasets, representations of a first plurality of sensor targetfeatures, the first plurality of sensor target features detectable bythe first sensor due to a type of the first plurality of sensor targetfeatures being detectable by sensors of a type of the first sensor. Atstep 1230, the internal computing system 110 of the vehicle 102identifies, in the second plurality of sensor capture datasets,representations of a second plurality of sensor target features, thesecond plurality of sensor target features detectable by the secondsensor due to a type of the second plurality of sensor target featuresbeing detectable by sensors of a type of the second sensor. Either ofsteps 1225 and 1230 can occur first, or they can occur at leastpartially in parallel.

The first plurality of sensor target features and the second pluralityof sensor target features may be on the same targets; for example, ifthe first sensor is a camera, and the second sensor is a LIDAR sensor,and plurality of sensor targets are the combined extrinsic calibrationtargets 250 of FIGS. 2F and 5A, then the first plurality of sensortarget features may be the visual markings (rings) 230 detectable by thecamera, while the second plurality of sensor target features are theapertures 225 detectable by the LIDAR. Alternately, the plurality ofsensor target features may be on different targets; for example, thefirst sensor may be a radar sensor and the first plurality of sensortarget features may be the trihedral radar calibration targets 220,while the second sensor may be any other sensor (e.g., camera, lidar)and the second plurality of sensor target features may be, for example,the visual markings (rings) 230 or apertures 225 of the combinedextrinsic calibration targets 250, or a pattern 210 of a cameraintrinsic target such as the targets 200A-D, or any other targetdescribed herein. If the first sensor is a visual camera, and the secondsensor is an distance measurement sensor, and plurality of sensortargets are the polyhedral sensor targets 255 of FIGS. 2G and 5B, thenthe first plurality of sensor target features may be the vertices 270detectable by the camera, while the second plurality of sensor targetfeatures are also the vertices 270 but as detectable by the distancemeasurement sensor (e.g., LIDAR sensor). Note that the first sensor andthe second sensor may be any combination of the possible types ofsensors 180 discussed herein.

At step 1235, the internal computing system 110 of the vehicle 102compares the relative positioning of the representations of the firstplurality of sensor target features and the representations of thesecond plurality of sensor target features to known relative positioningof the first plurality of sensor target features and the secondplurality of sensor target features. In some cases, the relativepositioning may be determined based on comparison of a position of aparticular point in one representation, such as the center, to aparticular point in the another representation to which it is beingcompared, such as the center. Points that can be used instead of thecenter may include or the highest point, lowest point, leftmost point,rightmost point, a point that is centered along one axis but notanother, a point at the widest portion of the representation, a point atthe narrowest portion of the representation, a point at a particularedge or vertex, or some combination thereof. At step 1240, the internalcomputing system 110 of the vehicle 102 generates a transformation basedon the comparison, such that the transformation aligns a first locationidentified by the first sensor and a second location identified by thesecond sensor.

As a first example, the first sensor may be a camera and the secondsensor may be a LIDAR sensor, and the first plurality of sensor targetfeatures and the second plurality of sensor target features may both befeatures of the combined extrinsic calibration targets 250 of FIGS. 2Fand 5 such that the first plurality of sensor target features are thevisual markings (rings) 230 detectable by the camera and the secondplurality of sensor target features are the apertures 225 detectable bythe LIDAR. In such a case, the internal computing system 110 of thevehicle 102 identifies a center of a particular aperture 225 based onthe LIDAR data, and identifies a center of a ring 230 based on thecamera data, compares these at step 1235 and identifies a relativedistance between the two locations based on the internal computingsystem 110's current geographic understanding of the calibrationenvironment. Because the internal computing system 110 understands thatthese two points should represent the same location in the real world(i.e., their relative positioning indicates no distance between them),the internal computing system 110 generates a transformation—which mayinclude, for example, a translation along X, Y, and/or Z dimensions, arotation along any axis, a warp or distortion filter, or somecombination thereof—that aligns these location points. That is, thetransformation translates (1) a mapping of a point from the one sensor'scapture data set to a real world position into (2) a mapping of a pointfrom the other sensor's capture data set to the same real worldposition. While, with just a pair or two of such points, there may bemultiple possible transformations that can perform this alignment, theinternal computing system 110 can generate a transformation that worksconsistently for an increasing number of pairs such sets of points—forexample, for each aperture 225 and ring 230 combinations of a target250, and for each target 250 in the calibration environment. As thenumber of pairs increases, the number of possible transformations thatcan successfully align these. Different sensors may map the world aroundthem differently; for example, if the camera includes a wide-angle lenswhile the other sensor (e.g., LIDAR) does not include an analogousdistortion effect, the transformation may include some radial movementor other compensation for distortion.

As a second example, the first sensor may be a radar sensor and thesecond sensor may be a LIDAR sensor, and the first plurality of sensortarget features may be trihedral radar calibration targets 220 while thesecond plurality of sensor target features may be apertures 225 of acombined target 250 or the planar boundaries of a substrate 205 of acamera target 200, each of which is a known distance away from thenearest trihedral radar calibration targets 220. In such a case, theinternal computing system 110 of the vehicle 102 identifies a locationof the trihedral radar calibration targets 220 based on radar sensordata and a location of the LIDAR target feature based on LIDAR sensordata, compares these at step 1235 and identifies a relative distancebetween the two locations based on the internal computing system 110'scurrent geographic understanding of the calibration environment. Becausethe internal computing system 110 understands that these two pointsshould be a known distance away in a particular direction at aparticular angle in the real world, the internal computing system 110generates a transformation—which may include, for example, a translationalong X, Y, and/or Z dimensions, a rotation along any axis, a warp ordistortion filter, or some combination thereof—that aligns theselocation points to match the same known distance away in the particulardirection at the particular angle as in the real world. While initiallythere may be multiple possible transformation that can perform this, theinternal computing system 110 can generate a transformation that worksconsistently for multiple such sets of points—for example, for eachtrihedral radar calibration target 220 and each nearby LIDAR targetfeature pair in the calibration environment.

As a third example, the first sensor may be a visual camera and thesecond sensor may be an infrared sensor (e.g., an infrared camera). Thefirst plurality of sensor target features may be dark markings asvisually detectable by the camera, while the second plurality of sensortarget features are the heat absorbed by the dark markings detectable bythe infrared sensor (e.g., infrared camera). The positions of the darkmarkings themselves as visually recognizable should be the same orsimilar to the positioning of the heat absorbed by the dark markingsdetectable by the infrared sensor, so the center of each can be foundand compared. Because the internal computing system 110 understands thatthese two points should represent the same location in the real world(i.e., their relative positioning indicates no distance between them),the internal computing system 110 generates a transformation—which mayinclude, for example, a translation along X, Y, and/or Z dimensions, arotation along any axis, a warp or distortion filter, or somecombination thereof—that aligns these location points.

At step 1245, the internal computing system 110 of the vehicle 102receives, from the first sensor and second sensor, post-calibrationsensor capture datasets captured by the first sensor and second sensorafter the calibration time period. At step 1250, the internal computingsystem 110 of the vehicle 102 applies the transformation generated instep 1240 to one or both of the post-calibration sensor capturedatasets. For example, a representation of a particular object can beidentified in a post-calibration sensor capture dataset captured by onesensor after calibration, and the transformation can be applied to findthe same object within another post-calibration sensor capture datasetcaptured by another sensor after calibration. A real-world position ofthe same object may be found relative to the vehicle 102 based onintrinsic calibration of at least one of the two sensors and/or based onthe transformation. In some cases, a representation of an entirespace—that is, a three-dimensional volume—in one post-calibration sensorcapture dataset captured by one sensor after calibration may then beidentified in another post-calibration sensor capture dataset capturedby another sensor by applying the transformation to multiple pointswithin the space. Important points, such as vertices (e.g., corners of aroom), edges (e.g., edges of a room), or other features may be selectedas at least some of these points. With two aligned representations of a3-D space, objects can be identified around the vehicle that might nototherwise be. For example, a pedestrian wearing all black might notvisually stand out against (e.g., contrast against) a background of anasphalt road at night, but a RADAR or LIDAR might easily identify thepedestrian, and the transformation will still allow the computer 110 ofthe vehicle 102 to understand where that pedestrian is in its camerafootage, allowing the vehicle to pay close attention to visual cues fromthe pedestrian that the RADAR or LIDAR might not catch or understand,such as presence or lack of a pet or small child accompanying thepedestrian. Developing the vehicle's understanding of its surroundingsby aligning real-world (and relative) mappings of the inputs it receivesfrom its sensors can save lives in the field of autonomous vehicles byallowing the best aspects of multiple sensors to complement one anotherto develop a comprehensive view of the vehicle's surroundings. No sensoris perfect at detecting everything—distance measurement sensors can seerange/depth but not color or brightness, and can have trouble seeingsmall or fast-moving objects—while cameras can see color and brightnessand visual features but can have trouble with depth perception. Thus,each sensor has its strengths, and the alignment made possible by theextrinsic calibration processes discussed in FIG. 12 can allow the bestaspects of each sensor (the “pros” of each sensor) to complement eachother and compensate for the downsides of each sensor (the “cons” ofeach sensor). Note that capture of data by the sensors 180 of thevehicle 102 may occur in parallel with calibration of the sensors 180 ofthe vehicle 102. While an initial transformation is generated at step1240, the vehicle 112 may continue to rotate, and its sensors 180 maycontinue to capture more sensor data, hence the dashed lines extendingback up to steps 1205 and 1210 and 1215 from step 1240. When step 1240is reached a second, third, or N^(th) time (where N is any integer over1), the transformation generated the first time step 1240 was reachedmay be updated, revised, and/or re-generated based on the newly capturedsensor data when step 1240 is reached again. Thus, the transformationbecomes more accurate as calibration continues.

For some additional context on extrinsic calibration, all sensors'extrinsic properties may include relative positions in X, Y, and/or Zdimensions, as well as roll, pitch, and/or yaw. Target and vehiclelocations are ground truthed via the mapping system discussed in step1010 and further as discussed with respect to the transformation of step1130 of FIG. 11. Sensors of the vehicle 102 and scene surveying system610 record the scene and each target is detected using a targetdetection method specific to that sensor and target pair. The measuredtarget location is compared against the mapped target location to derivethe total extrinsic sensor error:EXtr_(sensor)(R,t)=Σ_(target) ∥RC _(target) +t−D _(target)∥²

Where C_(target) is the measured location of the target and D_(target)is the mapped location of the target. We can collect the intrinsicsensor calibration data (as in FIG. 11) in a similar way, at each frameof recorded data the targets are detected and intrinsics are collected.These intrinsic sensor calibration data (as in FIG. 11) might be themeasured distance between pixel coordinates and the lines on a target,or lidar point coordinates and detected planar sides of a target. Thetotal error for a single sensor can be summarized as:ExtrIntr(R,t,α)_(sensor)=Intr_(sensor)(α)+γ_(sensor) Extr_(sensor)(R,t)

The weight γ_(sensor) determines the contribution of that sensor'sextrinsic parameter. By collecting the Extrintr for every sensor wedefine a global cost function that describes all intrinsics andextrinsics in the system. We can minimize the total expected error bytoggling the calibration parameters for each sensor [R,t,α] via a convexoptimization algorithm. The output of the sensor extrinsic calibrationsmay be a pair of rotation and translation matrices on a per sensor basiswith respect to the origin of the 3D space (e.g., as identified viaLIDAR).

After the calibration parameters are solved for, tests for the numericalsensitivity of the solution can be performed. This may include, forexample, verifying the Jacobian of the solution is near zero in alldirections and that the covariance of each parameter is reasonably small(e.g., below a threshold). More sophisticated routines that test forsensitivity to targets and constraints may also be performed.

FIG. 13 is a flow diagram illustrating operations for interactionsbetween the vehicle and the turntable. In particular, FIG. 13illustrates a process 1300.

At optional step 1305, the turntable 405, vehicle 102, or surveyingsystem 610 identifies that the vehicle 102 is positioned on the platformof the motorized turntable. This may be identified using pressuresensors 735 of the turntable 405, a GNSS or triangulation-basedpositioning receiver of the vehicle 102 compared to a known location ofthe turntable 405, image/infrared/radar/lidar data captured by thesurveying system 610 indicating that the vehicle 102 is positioned onmotorized turntable 405, or some combination thereof. In some cases, thepressure sensors 735 may be positioned under or beside the guiderailing, for example close behind the “stop” wall or incline, to ensurethat the vehicle will apply pressure to them. In other cases, the entireturntable is receptive as a pressure sensor. In any case, thisinformation is communicated to the dynamic scene control bridge 710and/or the computing device 745 of the turntable system 405, eitherwithin the turntable itself (if via sensors 735) or via the relevanttransceiver(s) of FIG. 7. In some cases, either sensor data capture bythe sensors of the vehicle 102 or rotation of the platform 420 of themotorized turntable 405 may automatically begin once the pressuresensors identify that the vehicle 102 is on the platform 420 and/or oncesensors identify that the vehicle 102 has stopped moving (e.g., IMU ofthe vehicle 102, regional pressure sensors of regions of the turntableplatform 420 surface, scene surveying system 610 camera, or somecombination thereof). Rotation of the platform 420 about the base 425may occur first before sensor data capture if, for example, calibrationis previously designated to start with the vehicle 102 rotated to aparticular rotation orientation or rotation position that is not thesame as the rotation orientation or rotation position that the vehicle102 is in when it drives (or is otherwise placed) onto the platform 420.

In some cases, the rotation of the platform 420 of the turntable 405about the base 425 via the motors 730 can manually be triggered insteadof being based on, and automatically triggered by, detection of thevehicle at step 1305, for example via an input received at the dynamicscene control bridge 710 and/or the computing device 745 of theturntable system 405 from a wired or wireless interface that itselfreceives an input from a human being, the wired or wireless interfacebeing for example a keyboard or touchscreen or mouse or remote controlcommunicating in a wired or wireless fashion with the dynamic scenecontrol bridge 710 and/or the computing device 745 of the turntablesystem 405.

At step 1310, one or more motors 730 of the motorized turntable 405 areactivated to rotate the platform 420 of the motorized turntable 405about the base 425 of the motorized turntable 405 (and therefore vehicle102 on top of the platform 420 as well) from a first rotationorientation to a second rotation orientation in response to detectionthat the vehicle is on the turntable. The one or more motors 730 may bedeactivated, causing the platform of the motorized turntable 405 (andtherefore vehicle 102 on top of the platform 420 as well) to stoprotating about the base 425 with the platform 420 in the secondorientation at the stop of rotation. The term “rotation orientation” maybe used to refer to an angle, or angular orientation, or angularposition. Other terms may be used in place of the term “rotationposition,” such as “angle,” “angular position,” “angular orientation,”“position,” or “orientation.” The first rotation orientation and thesecond rotation orientation may be a predetermined angle away from eachother, for example N degrees, where N is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,29, 30, or some amount in between any two of these numbers. The firstrotation orientation and the second rotation orientation may be an angleaway from each other that is determined by the internal computing system110 of the vehicle 102, or by the dynamic scene control bridge 710, orby the computing device 745 of the turntable system 405, or by somecombination thereof, based on which angle would likely be mostefficient, comprehensive, or an optimal balance thereof, in completingcalibration of the entire fields of view (FOV) of the sensors 180 of thevehicle 102.

At step 1315, the vehicle 102 uses its IMU (or other rotation detectiondevice) to check whether the vehicle 102 (and therefore the platform420) is still rotating. As the IMU is a collection of accelerometersand/or gyroscopes and/or other motion or rotation detection devices, thevehicle 102 can alternately separately use accelerometers and/orgyroscopes and/or other motion or rotation detection devices that areamong the vehicle 102's sensors 180 to determine this. Alternately, theturntable 405 may use one or more motion sensors of its own (e.g.,accelerometer, gyroscope, IMU, or any other motion sensor discussedherein) to identify whether the platform 420 of the turntable 405 isstill rotating about the base 425. Alternately still, the scenesurveying system 610 may use one or more cameras to visually identifywhether the platform of the turntable 405 and/or the vehicle 102 isstill rotating. In some cases, the device that detects that the vehicle102 and/or the platform 420 of the turntable 405 has stopped rotatingrelative to the base 425 (the vehicle computing system 110, thecomputing device 745 of the turntable 405, the scene surveying system610, and/or the dynamic scene control bridge 710) can send a signalidentifying the detected stop in rotation to any of the vehiclecomputing system 110, the computing device 745 of the turntable, thescene surveying system 610, or the dynamic scene control bridge 710.

If, at step 1320, the vehicle 102 or turntable 405 or scene surveyingsystem 610 determines that the rotation has stopped, step 1325 followsstep 1320. Otherwise, step 1315 follows step 1320.

In addition, we may use the vehicle 102's other sensors 180, such as oneor more cameras, infrared sensors, radar sensors, lidar sensors, sonarsensors, and/or sodar sensors instead of or in addition to the IMU,accelerometers, gyroscopes, and/or motion/rotation detection devices toidentify when the vehicle 102 (and thus the platform 420) is stillrotating relative to the base 425 or not. With all of these sensors,rotation may be identified based on whether regions of the calibrationenvironment that should be motionless—walls, the floor, the ceiling,targets that have not been configured and/or commanded to move, lightsources 620, the scene surveying system 610—are changing locationbetween sensor captures (indicating that the vehicle is rotating and/orin motion) or are stationary between sensor captures (indicating thatthe vehicle is stationary).

At step 1325, the vehicle captures sensor data using one or more of itssensors while the vehicle 102 is at the second position. If, at step1330, the internal computing device 110 of the vehicle 102 determinesthat it has finished capturing sensor data while vehicle is at thesecond rotational orientation/position, then step 1335 follows step1330, and optionally, the vehicle computing system 110 may send a sensorcapture confirmation signal to a computing device associated with theturntable 405, such as dynamic scene control bridge 710 and/or thecomputing device 745 of the turntable system 405. The sensor captureconfirmation signal may then be used as a signal that the turntable 405is allowed to begin (and/or should begin) rotation of the platform 420about the base 425 from the second rotation orientation to a nextrotation orientation. Otherwise, if sensor data capture is not completestep 1325 follows step 1330.

If, at step 1335, the internal computing device 110 of the vehicle 102determines that sufficient data has been captured by the vehicle 102'ssensors 180 to perform calibration—then no more rotations of theplatform 420 and the vehicle 102 about the base 425 are needed and step1340 follows step 1335, thus proceeding from sensor data capture tosensor calibration. Optionally, the vehicle computing system 110 maysend a sensor capture completion signal to a computing device associatedwith the turntable 405, such as dynamic scene control bridge 710 and/orthe computing device 745 of the turntable system 405. The sensor capturecompletion signal may then be used as a signal that the platform 420 ofthe turntable 405 is allowed to stop (and/or should stop) rotating aboutthe base 425 altogether to allow the vehicle 102 to exit the turntable405 and the calibration environment, or that the platform 425 of theturntable 405 is allowed to begin (and/or should begin) rotating aboutthe base 425 to an exit orientation that allow the vehicle 102 to exitthe turntable and the calibration environment (for example when thecalibration environment includes many targets around the turntable 405except for in an entrance/exit direction, as in FIG. 6 where an optimalentrance/exit direction is on the bottom-right due to lack of targetsand obstacles generally in that direction). Otherwise, if the internalcomputing device 110 of the vehicle 102 does not determine thatsufficient data has been captured by the vehicle 102's sensors 180 toperform calibration at step 1335, then step 1310 follows after step1335, to continue rotations of the platform 420 (and vehicle 102) aboutthe base 425 of the motorized turntable system 405. Depending on thesensors 180 on the vehicle 102 and the data captured by the sensors 180,the sensors 180 may require one or more full 360 degree rotations of thevehicle 102 on the platform 420, or may require less than one full 360degree rotation of the vehicle 102 on the platform 420. In oneembodiment, sufficient data for calibration of a sensor may mean datacorresponding to targets covering at least a subset of the completefield of view of a particular sensor (collectively over a number ofcaptures), with the subset reaching and/or exceeding a thresholdpercentage (e.g., 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%,85%, 90%, 95%, 99%, 100%).

Some sensors may require more data for calibration than others, andthus, one sensor may have captured sufficient data for calibration whileanother sensor might not. In such cases, step 1335 may refer to allsensors and thus go through the “NO” arrow if any of the sensors 180hasn't captured sufficient data. Alternately, a particular sensorcapturing sufficient data, or a majority of sensors capturing sufficientdata, may be the deciding factor toward “YES” or “NO.” In some cases,step 1335 may refer to each sensor separately, and once a particularsensor has captured sufficient data at step 1335, that sensor maycontinue on to step 1340 for calibration even if the vehicle 102 on theplatform 420 continues to rotate about the base 425 and the remainingsensors continue to capture data. Thus, step 1335 may enable staggeredcompletion of capture of sufficient data for different sensors atdifferent times.

In some cases, sensor data capture and sensor calibration occurs atleast partially in parallel; that is, a time period in which sensor datacapture occurs may at least partially overlap with a time period inwhich sensor calibration occurs. In such cases, the sensor may calibrateregion by region, for example by calibrating the sensor in one or moreregions in which the sensor detects (e.g., “sees”) targets for each datacapture until the entire point of view of the sensor, or some sufficientsubset is calibrated, with the subset reaching and/or exceeding athreshold percentage (e.g., 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%,75%, 80%, 85%, 90%, 95%, 99%, 100%). Calibration of each sensor may usea different threshold, or certain sensors may share a threshold. Whencalibration occurs in parallel with capture rather than calibration onthe whole sequentially following capture on the whole, step 1335 cansimply identify when calibration of one or more sensors has successfullycompleted, and treat that as a proxy for identifying whether sufficientdata is captured by those sensors to perform calibration.

In this case, however, step 1310 now rotates the vehicle 102 from thesecond position to a third position, and steps 1325 and 1330 refer tothe third position. The next time step 1310 is reached in this fashion,it now rotates the vehicle 102 from the third position to a fourthposition, and so on. In this way, step 1310 rotates the vehicle 102 onthe platform 420 about the base 425 from its current rotationalorientation/position to its next rotational orientation/position.

At step 1340, the internal computing device 110 of the vehicle 102proceeds on from sensor data capture to actual calibration of thesensors, for example as in steps 1125-1145 of FIG. 11 or steps 1225-1250of FIG. 12. To clarify, as discussed further above, capturing data viathe sensors 180 as in steps 1325-1335 and calibrating the sensors 180 asin step 1340 can be performed in parallel, between rotations of theplatform 420 and vehicle 102 about the base 425, or in any order thatcauses the process to be efficient. That is, calibration of datacaptured by a given one of the sensors 180 can begin immediately afterthe given sensor captures any new data, and can continue while thevehicle 102 is rotating about the base 425 on the platform 420 of theturntable 405 and while the sensors 180 capture further data. Becausecalibration and capture may be staggered and/or parallelized so thatsome further capture occurs after some calibration has started, dashedarrows extend from step 1340 to steps 1310 and 1325.

It should be understood that some of the steps of FIG. 13 (such as 1305,1315, 1320, 1330, and 1335) may be performed in some cases, and may beomitted in others.

FIG. 14A is a flow diagram illustrating operations for interactionsbetween the vehicle and a lighting system. In particular, FIG. 14Aillustrates a process 1400.

At step 1405, the vehicle 102 captures sensor datasets using its sensors180, for example as in steps 1010, 1110, 1210, 1215, and/or 1325. Atstep 1410, the internal computing system 110 of the vehicle 102identifies whether a characteristic of one or more sensor targets—inthis case lighting and/or heating conditions in at least one area of thecalibration environment that includes one or more sensor targets—aresuboptimal, at least for the purposes of calibration. This may includelighting from illuminated sensor targets that are backlit or frontlit aswell as sensor targets that are illuminated via exterior light sources620, such as spotlights. This may include heat sources 625 such as heatlamps. In some cases, the computer 110 of the vehicle 102 may identifythat a representation of a sensor target that is identified within asensor dataset (such as a photo or video) captured using the sensor(such as a camera) is suboptimal or not suitable for calibration, forexample because the sensor target is too dimly lit, too brightly lit,lit using an incorrect color, or lit from the wrong angle (e.g., causingglare, shadows, dimness, brightness, uneven lighting, or otherwiseaffecting the representation of the sensor target). Lighting conditionsmay be suboptimal because they may cause a sensor to not properly orclearly detect out one or more features of the sensor target, such as acheckerboard pattern 210A or ArUco pattern 210B or crosshair pattern210C of a camera target 200, or a shape 215 of a radar target 220, or aaperture 225/240 and/or marking 230 and/or target ID 235 of a combinedcamera/depth sensor target 250, or a pattern 210G of a polyhedral sensortarget 255. Heating conditions may be suboptimal on an IR sensor targetif any dark markings are not discernably hotter than the substrate 205of the IR sensor target as detected by the infrared sensor, whetherthat's because a heat source 625 is not hot enough or too hot.

If, at step 1410, the computer 110 of the vehicle 102 determines thatthe lighting conditions are suboptimal, then step 1410 is followed bystep 1415; otherwise, step 1410 is followed by step 1420, at which pointthe vehicle proceeds from capture to sensor calibration of its sensors,for example as in steps 1015-1025, 1125-1140, 1225-1250, and/or 1340.

Note that, as discussed with respect to FIG. 13, capture of data by thesensors 180 of the vehicle 102 may occur in parallel with calibration ofthe sensors 180 of the vehicle 102. This may cause calibration andcapture to be staggered and/or parallelized so that some further captureoccurs after some calibration has started, represented by the dashedarrow from step 1420 to step 1405.

At step 1415, the internal computing system 110 of the vehicle 102 sendsan environment adjustment signal or message to an environment adjustmentsystem (in this case the lighting/heating system 760) to activate one ormore actuators 762 and thereby adjust lighting conditions in the atleast one area of the calibration environment. The one or more actuators762 may control one or more motors associated with the lighting/heatingsystem 760, one or more switches associated with the lighting/heatingsystem 760, and/or one or more dimmers associated with thelighting/heating system 760. Upon receiving the environment adjustmentsignal or message from the vehicle 102, the lighting/heating system 760can activate the one or more actuators 762, and can thereby effect amodification to the characteristic (i.e., the lighting and/or heatingcondition) of the one or more sensor targets, for example by increasingor decreasing light and/or heat output of one or more light and/or heatsources 620/625, by moving one or more light and/or heat sources 620/625translationally, by rotating one or more light and/or heat sources620/625 (i.e., moving the one or more light and/or heat sources 620/625rotationally), by activating (i.e., turning on) one or more light and/orheat sources 620/625, by deactivating (i.e., turning off) one or morelight and/or heat sources 620/625, by changing a frequency of lightand/or heat emitted by the one or more light and/or heat sources620/625, by otherwise modifying the one or more light and/or heatsources 620/625, or some combination thereof. Note that an increase inlight and/or heat output as discussed herein may refer to increasinglight/heat output of one or more light and/or heat sources 620/625,activating one or more one or more light and/or heat sources 620/625,and/or moving one or more light and/or heat sources 620/625. Note that adecrease in light and/or heat output as discussed herein may refer todecreasing light and/or heat output one or more light and/or heatsources 620/625, deactivating one or more one or more light and/or heatsources 620/625, and/or moving one or more light and/or heat sources620/625. In some cases, the message of step 1415 may be sent to a mobilerobot 788 or otherwise to a computer 796 of the robotic scene controlsystem 790, which may cause one or more mobile robot 788 to drive tomove one or more light and/or heat sources 620/625, or to raise or lowerthe light and/or heat sources 620/625 using the screw or telescopiclifting mechanism, or to interact with a switch or other interfaceassociated with the light and/or heat source 620/625 to turn the lightand/or heat source 620/625 on or off, reduce the light and/or heatoutput and/or heat source 620/625, increase the light and/or heat outputof the light and/or heat source 620/625, filter the light and/or heatsource 620/625, change the output color of the light and/or heat source620/625, or perform any other action discussed with respect to step1415.

After step 1415, the process returns to 1405 to capture the sensor datawith newly-adjusted (i.e., optimized) lighting and/or heating. Thenewly-adjusted lighting and/or heating is then checked at step 1410 tosee whether the adjustment from step 1415 corrected the lighting and/orheating condition issue identified previously at step 1410 (leading tostep 1420), or if further adjustments are required (leading to step 1415once again).

FIG. 14B is a flow diagram illustrating operations for interactionsbetween the vehicle and a target control system. In particular, FIG. 14Billustrates a process 1450.

At step 1425, the vehicle 102 captures sensor datasets using its sensors180, for example as in steps 1010, 1110, 1210, 1215, 1325, and/or 1405.At step 1430, the internal computing system 110 of the vehicle 102identifies whether a characteristic of one or more sensor targets—inthis case sensor target positioning of at least one target in thecalibration environment is suboptimal, at least for the purposes ofcalibration. In some cases, the computer 110 of the vehicle 102 mayidentify that a representation of a sensor target that is identifiedwithin a sensor dataset (such as a photo or video or radar image/videoor lidar image/video) captured using the sensor (such as a camera orradar or lidar sensor) is suboptimal or not suitable for calibration,for example because the sensor target is located in a position and/orfacing an orientation in which the sensor cannot properly or clearlydetect out one or more features of the sensor target, such as acheckerboard pattern 210A or ArUco pattern 210B or crosshair pattern210C of a camera target 200, or a shape 215 of a radar target 220, or anaperture 225/240 and/or marking 230 and/or target ID 235 of a combinedcamera/depth sensor target 250, or a pattern 210G of a polyhedral sensortarget 255.

If, at step 1430, the computer 110 of the vehicle 102 determines thatthe sensor target positioning is sub-optimal, then step 1430 is followedby step 1435; otherwise, step 1430 is followed by step 1440, at whichpoint the vehicle proceeds from capture to sensor calibration of itssensors, for example as in steps 1015-1025, 1125-1140, 1225-1250, 1340,and/or 1420.

Note that, as discussed with respect to FIG. 13, capture of data by thesensors 180 of the vehicle 102 may occur in parallel with calibration ofthe sensors 180 of the vehicle 102. This may cause calibration andcapture to be staggered and/or parallelized so that some further captureoccurs after some calibration has started, represented by the dashedarrow from step 1440 to step 1425.

At step 1435, the internal computing system 110 of the vehicle 102 sendsan environment adjustment signal or message to an environment adjustmentsystem (in this case the target control system 770 and/or the roboticscene control system 790) to move the at least one sensor target to amore optimal position in the calibration environment, for example usingone or more mobile robots 788 of the robotic scene control system 790and/or by activating one or more actuators 774/792 of the target controlsystem 770 and/or of the robotic scene control system 790. The one ormore actuators 774/792 may control one or more motors and/or switchesassociated with the target control system 770 and/or the robotic scenecontrol system 790. Upon receiving the environment adjustment signal ormessage from the vehicle 102, the target control system 770 and/or ofthe robotic scene control system 790 can activate the one or moreactuators 774/792, and can thereby effect a modification to thecharacteristic (i.e., the location around the turntable, rotation aboutone or more axes, height above the surface that the turntable rests on)of the one or more sensor targets, for example by activating one or moremobile robots 788 to drive and therefore that translationally move oneor more targets and/or by activating one or more mobile robots 788 toraise or lower a target higher or lower and/or by activating one or moremotors of a mobile robot 788 or target control system 770 that rotateone or more targets about one or more axes.

After step 1435, the process returns to 1425 to capture the sensor datawith newly-moved (i.e., optimized) sensor target positioning. Thenewly-moved target positioning is then checked at step 1430 to seewhether the adjustment from step 1435 corrected the target positioningissue identified previously at step 1430 (leading to step 1440), or iffurther adjustments are required (leading to step 1435 once again).

In some cases, the adjustment(s) to lighting of FIG. 14A and theadjustment(s) to target positioning of FIG. 14B may both occur followingcapture of the same sensor dataset with the same sensor, In such cases,the checks of steps 1410 and 1430 may be performed repeatedly, onceafter each adjustment in either target positioning or lighting, sincemovement of a sensor target may correct or ameliorate issues withlighting, and on the other hand, adjustment of lighting may also corrector ameliorate issues with target positioning. In such cases, the sendingof messages, and the resulting adjustments, of steps 1415 and step 1435,can either be performed sequentially (and then tested at steps 1410and/or 1430), or can be performed in parallel (and then tested at steps1410 and/or 1430).

While various flow diagrams provided and described above, such as thosein FIGS. 10, 11, 12, 13, 14A, and 14B, may show a particular order ofoperations performed by some embodiments of the subject technology, itshould be understood that such order is exemplary. Alternativeembodiments may perform the operations in a different order, combinecertain operations, overlap certain operations, or some combinationthereof. It should be understood that unless disclosed otherwise, anyprocess illustrated in any flow diagram herein or otherwise illustratedor described herein may be performed by a machine, mechanism, and/orcomputing system #00 discussed herein, and may be performedautomatically (e.g., in response to one or more triggers/conditionsdescribed herein), autonomously, semi-autonomously (e.g., based onreceived instructions), or a combination thereof. Furthermore, anyaction described herein as occurring in response to one or moreparticular triggers/conditions should be understood to optionally occurautomatically response to the one or more particulartriggers/conditions.

As described herein, one aspect of the present technology is thegathering and use of data available from various sources to improvequality and experience. The present disclosure contemplates that in someinstances, this gathered data may include personal information. Thepresent disclosure contemplates that the entities involved with suchpersonal information respect and value privacy policies and practices,for example by encrypting such information.

FIG. 15 shows an example of computing system 1500, which can be forexample any computing device making up internal computing system 110,remote computing system 150, (potential) passenger device executingrideshare app 170, or any component thereof in which the components ofthe system are in communication with each other using connection 1505.Connection 1505 can be a physical connection via a bus, or a directconnection into processor 1510, such as in a chipset architecture.Connection 1505 can also be a virtual connection, networked connection,or logical connection.

In some embodiments, computing system 1500 is a distributed system inwhich the functions described in this disclosure can be distributedwithin a datacenter, multiple data centers, a peer network, etc. In someembodiments, one or more of the described system components representsmany such components each performing some or all of the function forwhich the component is described. In some embodiments, the componentscan be physical or virtual devices.

Example system 1500 includes at least one processing unit (CPU orprocessor) 1510 and connection 1505 that couples various systemcomponents including system memory 1515, such as read-only memory (ROM)1520 and random access memory (RAM) 1525 to processor 1510. Computingsystem 1500 can include a cache of high-speed memory 1512 connecteddirectly with, in close proximity to, or integrated as part of processor1510.

Processor 1510 can include any general purpose processor and a hardwareservice or software service, such as services 1532, 1534, and 1536stored in storage device 1530, configured to control processor 1510 aswell as a special-purpose processor where software instructions areincorporated into the actual processor design. Processor 1510 mayessentially be a completely self-contained computing system, containingmultiple cores or processors, a bus, memory controller, cache, etc. Amulti-core processor may be symmetric or asymmetric.

To enable user interaction, computing system 1500 includes an inputdevice 1545, which can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech, etc. Computingsystem 1500 can also include output device 1535, which can be one ormore of a number of output mechanisms known to those of skill in theart. In some instances, multimodal systems can enable a user to providemultiple types of input/output to communicate with computing system1500. Computing system 1500 can include communications interface 1540,which can generally govern and manage the user input and system output.The communication interface may perform or facilitate receipt and/ortransmission wired or wireless communications via wired and/or wirelesstransceivers, including those making use of an audio jack/plug, amicrophone jack/plug, a universal serial bus (USB) port/plug, an Apple®Lightning® port/plug, an Ethernet port/plug, a fiber optic port/plug, aproprietary wired port/plug, a BLUETOOTH® wireless signal transfer, aBLUETOOTH® low energy (BLE) wireless signal transfer, an IBEACON®wireless signal transfer, a radio-frequency identification (RFID)wireless signal transfer, near-field communications (NFC) wirelesssignal transfer, dedicated short range communication (DSRC) wirelesssignal transfer, 802.11 Wi-Fi wireless signal transfer, wireless localarea network (WLAN) signal transfer, Visible Light Communication (VLC),Worldwide Interoperability for Microwave Access (WiMAX), Infrared (IR)communication wireless signal transfer, Public Switched TelephoneNetwork (PSTN) signal transfer, Integrated Services Digital Network(ISDN) signal transfer, 3G/4G/5G/LTE cellular data network wirelesssignal transfer, ad-hoc network signal transfer, radio wave signaltransfer, microwave signal transfer, infrared signal transfer, visiblelight signal transfer, ultraviolet light signal transfer, wirelesssignal transfer along the electromagnetic spectrum, or some combinationthereof. The communications interface 1540 may also include one or moreGlobal Navigation Satellite System (GNSS) receivers or transceivers thatare used to determine a location of the computing system 1500 based onreceipt of one or more signals from one or more satellites associatedwith one or more GNSS systems. GNSS systems include, but are not limitedto, the US-based Global Positioning System (GPS), the Russia-basedGlobal Navigation Satellite System (GLONASS), the China-based BeiDouNavigation Satellite System (BDS), and the Europe-based Galileo GNSS.There is no restriction on operating on any particular hardwarearrangement, and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

Storage device 1530 can be a non-volatile and/or non-transitory and/orcomputer-readable memory device and can be a hard disk or other types ofcomputer readable media which can store data that are accessible by acomputer, such as magnetic cassettes, flash memory cards, solid statememory devices, digital versatile disks, cartridges, a floppy disk, aflexible disk, a hard disk, magnetic tape, a magnetic strip/stripe, anyother magnetic storage medium, flash memory, memristor memory, any othersolid-state memory, a compact disc read only memory (CD-ROM) opticaldisc, a rewritable compact disc (CD) optical disc, digital video disk(DVD) optical disc, a blu-ray disc (BDD) optical disc, a holographicoptical disk, another optical medium, a secure digital (SD) card, amicro secure digital (microSD) card, a Memory Stick® card, a smartcardchip, a EMV chip, a subscriber identity module (SIM) card, amini/micro/nano/pico SIM card, another integrated circuit (IC)chip/card, random access memory (RAM), static RAM (SRAM), dynamic RAM(DRAM), read-only memory (ROM), programmable read-only memory (PROM),erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), flash EPROM (FLASHEPROM), cachememory (L1/L2/L3/L4/L5/L#), resistive random-access memory (RRAM/ReRAM),phase change memory (PCM), spin transfer torque RAM (STT-RAM), anothermemory chip or cartridge, and/or a combination thereof.

The storage device 1530 can include software services, servers,services, etc., that when the code that defines such software isexecuted by the processor 1510, it causes the system to perform afunction. In some embodiments, a hardware service that performs aparticular function can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as processor 1510, connection 1505, output device 1535,etc., to carry out the function.

For clarity of explanation, in some instances, the present technologymay be presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

Any of the steps, operations, functions, or processes described hereinmay be performed or implemented by a combination of hardware andsoftware services or services, alone or in combination with otherdevices. In some embodiments, a service can be software that resides inmemory of a client device and/or one or more servers of a contentmanagement system and perform one or more functions when a processorexecutes the software associated with the service. In some embodiments,a service is a program or a collection of programs that carry out aspecific function. In some embodiments, a service can be considered aserver. The memory can be a non-transitory computer-readable medium.

In some embodiments, the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer-readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The executable computer instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, solid-state memory devices, flash memory, USB devices providedwith non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include servers,laptops, smartphones, small form factor personal computers, personaldigital assistants, and so on. The functionality described herein alsocan be embodied in peripherals or add-in cards. Such functionality canalso be implemented on a circuit board among different chips ordifferent processes executing in a single device, by way of furtherexample.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

As described herein, one aspect of the present technology is thegathering and use of data available from various sources to improvequality and experience. The present disclosure contemplates that in someinstances, this gathered data may include personal information. Thepresent disclosure contemplates that the entities involved with suchpersonal information respect and value privacy policies and practices.

What is claimed is:
 1. A system for sensor calibration, the systemcomprising: a camera coupled to a housing that moves between a pluralityof positions over a course of a calibration time period, wherein thecamera captures a plurality of camera datasets corresponding to theplurality of positions over the course of the calibration time period; adistance measurement sensor coupled to the housing, wherein the distancemeasurement sensor captures a plurality of distance measurement datasetscorresponding to the plurality of positions over the course of thecalibration time period; a memory storing instructions; and a processorthat executes the instructions, wherein execution of the instructions bythe processor causes the processor to: receive the plurality of cameradatasets from the camera, identify, within at least a subset of theplurality of camera datasets, a visual representation of a polyhedralsensor target, wherein a plurality of surfaces of the polyhedral sensortarget are visually discernable from one another in the visualrepresentation of the polyhedral sensor target, receive the plurality ofdistance measurement datasets from the distance measurement sensor,identify, within at least a subset of the plurality of distancemeasurement datasets, a distance measurement representation of thepolyhedral sensor target, wherein the plurality of surfaces of thepolyhedral sensor target are discernable from one another in thedistance measurement representation of the polyhedral sensor target, andcalibrate interpretation of data captured by the camera and by thedistance measurement sensor based on the visual representation of thepolyhedral sensor target and the distance measurement representation ofthe polyhedral sensor target.
 2. The system of claim 1, wherein thehousing is a vehicle.
 3. The system of claim 2, wherein the vehicle isan automobile.
 4. The system of claim 1, wherein the distancemeasurement sensor includes a light measurement and ranging (LIDAR)sensor.
 5. The system of claim 1, wherein the distance measurementsensor includes a radio measurement and ranging (RADAR) sensor.
 6. Thesystem of claim 1, wherein the polyhedral sensor target includes one ormore markings on the plurality of surfaces, wherein the plurality ofsurfaces of the polyhedral sensor target are visually discernable fromone another in the visual representation of the polyhedral sensor targetdue to the one or more markings.
 7. The system of claim 6, wherein theone or more markings form a checkerboard pattern.
 8. The system of claim1, further comprising: a heat source that outputs heat toward thepolyhedral sensor target, wherein one or more markings on the pluralityof surfaces absorb more of the heat from the heat source than asubstrate of the plurality of surfaces, and wherein the camera is aninfrared camera.
 9. The system of claim 1, further comprising a motorthat rotates the polyhedral sensor target.
 10. The system of claim 1,wherein a first surface of the plurality of surfaces is flat.
 11. Thesystem of claim 1, wherein a first surface of the plurality of surfacesis curved.
 12. The system of claim 1, wherein calibrating interpretationof the data captured by the camera and by the distance measurementsensor comprises: identifying a visual representation of a vertex of thepolyhedral sensor target in the visual representation of the polyhedralsensor target; identifying a distance measurement representation of thevertex of the polyhedral sensor target in the distance measurementrepresentation of the polyhedral sensor target; and mapping the visualrepresentation of the vertex and the distance measurement representationof the vertex to a single location.
 13. The system of claim 1, furthercomprising a motorized turntable, wherein the housing moves between theplurality of positions over the course of the calibration time periodbecause the housing is rotated into each of the plurality of positionsby the motorized turntable over the course of the calibration timeperiod.
 14. The system of claim 1, wherein the polyhedral sensor targetis cubic.
 15. A method for sensor calibration, the method comprising:receiving a plurality of camera datasets from a camera coupled to ahousing, wherein the housing moves between a plurality of positions andthe camera captures the plurality of camera datasets over a course of acalibration time period, wherein each of the plurality of positionscorresponds to at least one of the plurality of camera datasets;identifying, within at least a subset of the plurality of cameradatasets, a visual representation of a polyhedral sensor target, whereina plurality of surfaces of the polyhedral sensor target are visuallydiscernable from one another in the visual representation of thepolyhedral sensor target; receiving a plurality of distance measurementdatasets from a distance measurement sensor coupled to the housing,wherein the distance measurement sensor captures the plurality ofdistance measurement datasets over the course of the calibration timeperiod, wherein each of the plurality of positions corresponds to atleast one of the plurality of distance measurement datasets;identifying, within at least a subset of the plurality of distancemeasurement datasets, a distance measurement representation of thepolyhedral sensor target, wherein the plurality of surfaces of thepolyhedral sensor target are discernable from one another in thedistance measurement representation of the polyhedral sensor target; andcalibrating interpretation of data captured by the camera and by thedistance measurement sensor based on the visual representation of thepolyhedral sensor target and the distance measurement representation ofthe polyhedral sensor target.
 16. The method of claim 15, furthercomprising: receiving a plurality of secondary distance measurementdatasets from a secondary distance measurement sensor coupled to thehousing, wherein the secondary distance measurement sensor captures theplurality of secondary distance measurement datasets over the course ofthe calibration time period, wherein each of the plurality of positionscorresponds to at least one of the plurality of secondary distancemeasurement datasets; identifying, within at least a subset of theplurality of secondary distance measurement datasets, a secondarydistance measurement representation of a secondary sensor target with aknown relative location to the polyhedral sensor target; and calibratinginterpretation of data captured by the camera and by the distancemeasurement sensor and by the secondary distance measurement sensorbased on the visual representation of the polyhedral sensor target, thedistance measurement representation of the polyhedral sensor target, andthe secondary distance measurement representation of the secondarysensor target.
 17. The method of claim 15, wherein the polyhedral sensortarget includes one or more markings on the plurality of surfaces,wherein the plurality of surfaces of the polyhedral sensor target arevisually discernable from one another in the visual representation ofthe polyhedral sensor target due to the one or more markings.
 18. Themethod of claim 15, wherein calibrating interpretation of the datacaptured by the camera and by the distance measurement sensor comprises:identifying a visual representation of a vertex of the polyhedral sensortarget in the visual representation of the polyhedral sensor target;identifying a distance measurement representation of the vertex of thepolyhedral sensor target in the distance measurement representation ofthe polyhedral sensor target; and mapping the visual representation ofthe vertex and the distance measurement representation of the vertex toa single location.
 19. The method of claim 15, further comprising:actuating a motorized turntable, wherein the housing moves between theplurality of positions over the course of the calibration time periodbecause the housing is rotated into each of the plurality of positionsby the motorized turntable over the course of the calibration timeperiod.
 20. A non-transitory computer readable storage medium havingembodied thereon a program, wherein the program is executable by aprocessor to perform a method of sensor calibration, the methodcomprising: receiving a plurality of camera datasets from a cameracoupled to a housing, wherein the housing moves between a plurality ofpositions and the camera captures the plurality of camera datasets overa course of a calibration time period, wherein each of the plurality ofpositions corresponds to at least one of the plurality of cameradatasets; identifying, within at least a subset of the plurality ofcamera datasets, a visual representation of a polyhedral sensor target,wherein a plurality of surfaces of the polyhedral sensor target arevisually discernable from one another in the visual representation ofthe polyhedral sensor target; receiving a plurality of distancemeasurement datasets from a distance measurement sensor coupled to thehousing, wherein the distance measurement sensor captures the pluralityof distance measurement datasets over the course of the calibration timeperiod, wherein each of the plurality of positions corresponds to atleast one of the plurality of distance measurement datasets;identifying, within at least a subset of the plurality of distancemeasurement datasets, a distance measurement representation of thepolyhedral sensor target, wherein the plurality of surfaces of thepolyhedral sensor target are discernable from one another in thedistance measurement representation of the polyhedral sensor target; andcalibrating interpretation of data captured by the camera and by thedistance measurement sensor based on the visual representation of thepolyhedral sensor target and the distance measurement representation ofthe polyhedral sensor target.